diff --git a/Dockerfile b/Dockerfile
index 90740461..f7db3fff 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,25 +1,24 @@
 FROM python:3.8.16-slim
 
 # set environment variables
-ENV PYTHONPATH "${PYTHONPATH}:/usr/src/app"
-ENV PATH /usr/local/bin:$PATH
-ENV LANG C.UTF-8
-ENV PYTHONUNBUFFERED 1
-ENV PYTHONDONTWRITEBYTECODE 1
-
+ENV PATH="/usr/local/bin:$PATH"
+ENV LANG="C.UTF-8"
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONDONTWRITEBYTECODE=1
 # set work directory
 WORKDIR /usr/src/app
 
-COPY ./README.md /tmp/README.md
 COPY ./setup.py /tmp/setup.py
 
 # install git
 RUN apt-get update && apt-get install git -y
 
-COPY ./src/requirements.txt /tmp/requirements.txt
+
 RUN apt-get update && apt-get install ffmpeg libsm6 libxext6  -y\
-    && pip install --upgrade pip setuptools wheel \
-    && pip install --default-timeout=500 -r /tmp/requirements.txt \
+    && pip install --upgrade pip setuptools wheel 
+
+COPY ./src/requirements.txt /tmp/requirements.txt
+RUN pip install --default-timeout=500 -r /tmp/requirements.txt \
     && pip cache purge \
     && rm -rf /root/.cache/pip
 
diff --git a/Makefile b/Makefile
index 3f09854a..d740a3c0 100644
--- a/Makefile
+++ b/Makefile
@@ -40,7 +40,6 @@ build-optional-lib:
 
 # Run the engine wrapper
 run:
-	bash scripts/setup-docker-compose.sh
 	docker build . -t pyronear/pyro-engine:latest
 	docker compose up -d
 
diff --git a/docker-compose.yml b/docker-compose.yml
index ed13cbba..1b416755 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -13,10 +13,6 @@ services:
       - ./data:/usr/src/app/data
     restart: always
     network_mode: host
-    deploy:
-      resources:
-        limits:
-          cpus: "3"
     logging:
       driver: "json-file"
       options:
diff --git a/pyproject.toml b/pyproject.toml
index f3ff323e..79214e25 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,12 +30,9 @@ classifiers = [
 ]
 dynamic = ["version"]
 dependencies = [
-    "Pillow>=8.4.0",
-    "onnxruntime>=1.10.0,<2.0.0",
-    "numpy>=1.19.5,<2.0.0",
+    "ultralytics==8.2.50",
     "pyroclient @ git+https://github.com/pyronear/pyro-api.git@767be30a781b52b29d68579d543e3f45ac8c4713#egg=pyroclient&subdirectory=client",
     "requests>=2.20.0,<3.0.0",
-    "opencv-python==4.5.5.64",
     "tqdm>=4.62.0",
     "huggingface_hub==0.23.1", 
 ]
@@ -43,6 +40,7 @@ dependencies = [
 [project.optional-dependencies]
 test = [
     "pytest>=5.3.2",
+    "pytest-asyncio>=0.14.0",
     "coverage[toml]>=4.5.4",
     "requests>=2.20.0,<3.0.0",
     "python-dotenv>=0.15.0",
@@ -66,6 +64,7 @@ docs = [
 dev = [
     # test
     "pytest>=5.3.2",
+    "pytest-asyncio>=0.14.0",
     "coverage[toml]>=4.5.4",
     "requests>=2.20.0,<3.0.0",
     # style
@@ -93,7 +92,6 @@ zip-safe = true
 [tool.setuptools.packages.find]
 exclude = ["docs*", "scripts*", "tests*", "src*"]
 
-
 [tool.mypy]
 files = "pyroengine/"
 show_error_codes = true
diff --git a/pyroengine/core.py b/pyroengine/core.py
index 8789acca..8ad82f38 100644
--- a/pyroengine/core.py
+++ b/pyroengine/core.py
@@ -3,24 +3,20 @@
 # This program is licensed under the Apache License 2.0.
 # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
 
+import asyncio
 import logging
-import signal
+import time
 from datetime import datetime
-from multiprocessing import Manager, Pool
-from multiprocessing import Queue as MPQueue
-from types import FrameType
-from typing import List, Optional, Tuple, cast
+from typing import Any, List
 
 import numpy as np
 import urllib3
-from PIL import Image
 
 from .engine import Engine
 from .sensors import ReolinkCamera
 
 __all__ = ["SystemController", "is_day_time"]
 
-
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
 # Configure logging
@@ -28,26 +24,28 @@
 
 
 def is_day_time(cache, frame, strategy, delta=0):
-    """This function allows to know if it is daytime or not. We have two strategies.
-    The first one is to take the current time and compare it to the sunset time.
-    The second is to see if we have a color image. The ir cameras switch to ir mode at night and
-    therefore produce black and white images. This function can use one or more strategies depending on the use case.
+    """
+    Determines if it is daytime using specified strategies.
+
+    Strategies:
+    1. Time-based: Compares the current time with sunrise and sunset times.
+    2. IR-based: Analyzes the color of the image; IR cameras produce black and white images at night.
 
     Args:
-        cache (Path): cache folder where sunset_sunrise.txt is located
-        frame (PIL image): frame to analyze with ir strategy
-        strategy (str): Strategy to define day time [None, time, ir or both]
-        delta (int): delta before and after sunset / sunrise in sec
+        cache (Path): Cache folder where `sunset_sunrise.txt` is located.
+        frame (PIL.Image): Frame to analyze with the IR strategy.
+        strategy (str): Strategy to define daytime ("time", "ir", or "both").
+        delta (int): Time delta in seconds before and after sunrise/sunset.
 
     Returns:
-        bool: is day time
+        bool: True if it is daytime, False otherwise.
     """
     is_day = True
     if strategy in ["both", "time"]:
         with open(cache.joinpath("sunset_sunrise.txt")) as f:
             lines = f.readlines()
-        sunrise = datetime.strptime(lines[0][:-1], "%H:%M")
-        sunset = datetime.strptime(lines[1][:-1], "%H:%M")
+        sunrise = datetime.strptime(lines[0].strip(), "%H:%M")
+        sunset = datetime.strptime(lines[1].strip(), "%H:%M")
         now = datetime.strptime(datetime.now().isoformat().split("T")[1][:5], "%H:%M")
         if (now - sunrise).total_seconds() < -delta or (sunset - now).total_seconds() < -delta:
             is_day = False
@@ -60,95 +58,89 @@ def is_day_time(cache, frame, strategy, delta=0):
     return is_day
 
 
-def handler(signum: int, frame: Optional[FrameType]) -> None:
-    """
-    Signal handler for timeout.
-
-    Args:
-        signum (int): The signal number.
-        frame (Optional[FrameType]): The current stack frame (or None).
-    """
-    raise Exception("Analyze stream timeout")
-
-
-def capture_camera_image(args: Tuple[ReolinkCamera, MPQueue]) -> None:
+async def capture_camera_image(camera: ReolinkCamera, image_queue: asyncio.Queue) -> None:
     """
     Captures an image from the camera and puts it into a queue.
 
     Args:
-        args (tuple): A tuple containing the camera instance and a queue.
+        camera (ReolinkCamera): The camera instance.
+        image_queue (asyncio.Queue): The queue to put the captured image.
     """
-    camera, queue = args
-
     cam_id = camera.ip_address
     try:
         if camera.cam_type == "ptz":
-            for pose_id in camera.cam_poses:
+            for idx, pose_id in enumerate(camera.cam_poses):
                 cam_id = f"{camera.ip_address}_{pose_id}"
-                frame = camera.capture(pose_id)
+                frame = camera.capture()
+                # Move camera to the next pose to avoid waiting
+                next_pos_id = camera.cam_poses[(idx + 1) % len(camera.cam_poses)]
+                camera.move_camera("ToPos", idx=int(next_pos_id), speed=50)
                 if frame is not None:
-                    queue.put((cam_id, frame))
+                    await image_queue.put((cam_id, frame))
+                    await asyncio.sleep(0)  # Yield control
         else:
             frame = camera.capture()
             if frame is not None:
-                queue.put((cam_id, frame))
+                await image_queue.put((cam_id, frame))
+                await asyncio.sleep(0)  # Yield control
     except Exception as e:
         logging.exception(f"Error during image capture from camera {cam_id}: {e}")
 
 
 class SystemController:
     """
-    Implements the full system controller for capturing and analyzing camera streams.
+    Controls the system for capturing and analyzing camera streams.
 
     Attributes:
         engine (Engine): The image analyzer engine.
-        cameras (List[ReolinkCamera]): The list of cameras to get the visual streams from.
+        cameras (List[ReolinkCamera]): List of cameras to capture streams from.
     """
 
     def __init__(self, engine: Engine, cameras: List[ReolinkCamera]) -> None:
         """
-        Initializes the SystemController with an engine and a list of cameras.
+        Initializes the SystemController.
 
         Args:
             engine (Engine): The image analyzer engine.
-            cameras (List[ReolinkCamera]): The list of cameras to get the visual streams from.
+            cameras (List[ReolinkCamera]): List of cameras to capture streams from.
         """
         self.engine = engine
         self.cameras = cameras
         self.day_time = True
 
-    def capture_images(self) -> MPQueue:
+    async def capture_images(self, image_queue: asyncio.Queue) -> None:
         """
-        Captures images from all cameras using multiprocessing.
+        Captures images from all cameras using asyncio.
 
-        Returns:
-            MPQueue: A queue containing the captured images and their camera IDs.
+        Args:
+            image_queue (asyncio.Queue): The queue to put the captured images.
         """
+        tasks = [capture_camera_image(camera, image_queue) for camera in self.cameras]
+        await asyncio.gather(*tasks)
 
-        manager = Manager()
-        queue: MPQueue = cast(MPQueue, manager.Queue())  # Cast to MPQueue
-
-        # Create a list of arguments to pass to capture_camera_image
-        args_list: List[Tuple[ReolinkCamera, MPQueue]] = [(camera, queue) for camera in self.cameras]
-
-        # Use a pool of processes to capture images concurrently
-        with Pool(processes=len(self.cameras)) as pool:
-            pool.map(capture_camera_image, args_list)
-
-        return queue
-
-    def analyze_stream(self, img: Image.Image, cam_id: str) -> None:
+    async def analyze_stream(self, image_queue: asyncio.Queue) -> None:
         """
-        Analyzes the image stream from a specific camera.
+        Analyzes the image stream from the queue.
 
         Args:
-            img (Image.Image): The image to analyze.
-            cam_id (str): The ID of the camera.
+            image_queue (asyncio.Queue): The queue with images to analyze.
         """
-        # Run the prediction using the engine
-        self.engine.predict(img, cam_id)
+        while True:
+            item = await image_queue.get()
+            if item is None:
+                break
+            cam_id, frame = item
+            try:
+                self.engine.predict(frame, cam_id)
+            except Exception as e:
+                logging.error(f"Error running prediction: {e}")
+            finally:
+                image_queue.task_done()  # Mark the task as done
 
     def check_day_time(self) -> None:
+        """
+        Checks and updates the day_time attribute based on the current frame.
+        """
         try:
             frame = self.cameras[0].capture()
             if frame is not None:
@@ -156,43 +148,31 @@ def check_day_time(self) -> None:
         except Exception as e:
             logging.exception(f"Exception during initial day time check: {e}")
 
-    def run(self, period: int = 30) -> None:
+    async def run(self, period: int = 30) -> None:
         """
         Captures and analyzes all camera streams, then processes alerts.
 
         Args:
             period (int): The time period between captures in seconds.
         """
-
         try:
-            # Set the signal alarm
-            signal.signal(signal.SIGALRM, handler)
-            signal.alarm(period)
-
-            if not self.day_time:
-                self.check_day_time()
+            self.check_day_time()
 
             if self.day_time:
-                # Capture images
-                queue = None
-                try:
-                    queue = self.capture_images()
-                except Exception as e:
-                    logging.error(f"Error capturing images: {e}")
-
-                # Analyze each captured frame
-                if queue:
-                    while not queue.empty():
-                        cam_id, frame = queue.get()
-                        try:
-                            if frame is not None:
-                                self.analyze_stream(frame, cam_id)
-                        except Exception as e:
-                            logging.error(f"Error running prediction: {e}")
-
-                    # Use the last frame to check if it's day_time
-                    if frame is not None:
-                        self.day_time = is_day_time(None, frame, "ir")
+                image_queue: asyncio.Queue[Any] = asyncio.Queue()
+
+                # Start the image processor task
+                processor_task = asyncio.create_task(self.analyze_stream(image_queue))
+
+                # Capture images concurrently
+                await self.capture_images(image_queue)
+
+                # Wait for the image processor to finish processing
+                await image_queue.join()  # Ensure all tasks are marked as done
+
+                # Signal the image processor to stop processing
+                await image_queue.put(None)
+                await processor_task  # Ensure the processor task completes
 
                 # Process alerts
                 try:
@@ -200,10 +180,24 @@ def run(self, period: int = 30) -> None:
                 except Exception as e:
                     logging.error(f"Error processing alerts: {e}")
 
-            # Disable the alarm
-            signal.alarm(0)
-        except Exception:
-            logging.warning("Analyze stream timeout")
+        except Exception as e:
+            logging.warning(f"Analyze stream error: {e}")
+
+    async def main_loop(self, period: int) -> None:
+        """
+        Main loop to capture and process images at regular intervals.
+
+        Args:
+            period (int): The time period between captures in seconds.
+        """
+        while True:
+            start_ts = time.time()
+            await self.run(period)
+            # Sleep only once all images are processed
+            loop_time = time.time() - start_ts
+            sleep_time = max(period - (loop_time), 0)
+            logging.info(f"Loop run under {loop_time:.2f} seconds, sleeping for {sleep_time:.2f}")
+            await asyncio.sleep(sleep_time)
 
     def __repr__(self) -> str:
         """
diff --git a/pyroengine/engine.py b/pyroengine/engine.py
index aec5e371..6c71f0f8 100644
--- a/pyroengine/engine.py
+++ b/pyroengine/engine.py
@@ -15,7 +15,6 @@
 from pathlib import Path
 from typing import Any, Dict, Optional, Tuple
 
-import cv2  # type: ignore[import-untyped]
 import numpy as np
 from PIL import Image
 from pyroclient import client
@@ -57,12 +56,12 @@ class Engine:
         >>> "cam_id_1": {'login':'log1', 'password':'pwd1'},
         >>> "cam_id_2": {'login':'log2', 'password':'pwd2'},
         >>> }
-        >>> pyroEngine = Engine("data/model.onnx", 0.25, 'https://api.pyronear.org', cam_creds, 48.88, 2.38)
+        >>> pyroEngine = Engine(None, 0.25, 'https://api.pyronear.org', cam_creds, 48.88, 2.38)
     """
 
     def __init__(
         self,
-        model_path: Optional[str] = "data/model.onnx",
+        model_path: Optional[str] = None,
         conf_thresh: float = 0.25,
         api_url: Optional[str] = None,
         cam_creds: Optional[Dict[str, Dict[str, str]]] = None,
@@ -83,7 +82,7 @@ def __init__(
         """Init engine"""
         # Engine Setup
 
-        self.model = Classifier(model_path)
+        self.model = Classifier(model_path=model_path)
         self.conf_thresh = conf_thresh
 
         # API Setup
@@ -120,12 +119,12 @@ def __init__(
                     "ongoing": False,
                 }
 
-        self.occlusion_masks = {"-1": None}
+        self.occlusion_masks: Dict[str, Optional[np.ndarray]] = {"-1": None}
         if isinstance(cam_creds, dict):
             for cam_id in cam_creds:
                 mask_file = cache_folder + "/occlusion_masks/" + cam_id + ".jpg"
                 if os.path.isfile(mask_file):
-                    self.occlusion_masks[cam_id] = cv2.imread(mask_file, 0)
+                    self.occlusion_masks[cam_id] = np.array(Image.open(mask_file).convert(("L")))
                 else:
                     self.occlusion_masks[cam_id] = None
 
diff --git a/pyroengine/sensors.py b/pyroengine/sensors.py
index bf4b65c2..62131006 100644
--- a/pyroengine/sensors.py
+++ b/pyroengine/sensors.py
@@ -55,6 +55,9 @@ def __init__(
         self.cam_poses = cam_poses if cam_poses is not None else []
         self.protocol = protocol
 
+        if len(self.cam_poses):
+            self.move_camera("ToPos", idx=int(self.cam_poses[0]), speed=50)
+
     def _build_url(self, command: str) -> str:
         """Constructs a URL for API commands to the camera."""
         return (
@@ -198,3 +201,33 @@ def delete_ptz_preset(self, idx: int):
         response = requests.post(url, json=data, verify=False)  # nosec: B501
         # Utilizing the shared response handling method
         self._handle_response(response, f"Preset {idx} deleted successfully.")
+
+    def reboot_camera(self):
+        url = self._build_url("Reboot")
+        data = [{"cmd": "Reboot"}]
+        response = requests.post(url, json=data, verify=False)
+        return self._handle_response(response, "Camera reboot initiated successfully.")
+
+    def get_auto_focus(self):
+        url = self._build_url("GetAutoFocus")
+        data = [{"cmd": "GetAutoFocus", "action": 1, "param": {"channel": 0}}]
+        response = requests.post(url, json=data, verify=False)
+        return self._handle_response(response, "Fetched AutoFocus settings successfully.")
+
+    def set_auto_focus(self, disable: bool):
+        url = self._build_url("SetAutoFocus")
+        data = [{"cmd": "SetAutoFocus", "action": 0, "param": {"AutoFocus": {"channel": 0, "disable": int(disable)}}}]
+        response = requests.post(url, json=data, verify=False)
+        return self._handle_response(response, "Set AutoFocus settings successfully.")
+
+    def start_zoom_focus(self, position: int):
+        url = self._build_url("StartZoomFocus")
+        data = [
+            {
+                "cmd": "StartZoomFocus",
+                "action": 0,
+                "param": {"ZoomFocus": {"channel": 0, "pos": position, "op": "ZoomPos"}},
+            }
+        ]
+        response = requests.post(url, json=data, verify=False)
+        return self._handle_response(response, "Started ZoomFocus successfully.")
diff --git a/pyroengine/utils.py b/pyroengine/utils.py
index ba68d4a3..30296353 100644
--- a/pyroengine/utils.py
+++ b/pyroengine/utils.py
@@ -4,65 +4,10 @@
 # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
 
 
-import cv2  # type: ignore[import-untyped]
 import numpy as np
 from tqdm import tqdm  # type: ignore[import-untyped]
 
-__all__ = ["nms", "xywh2xyxy", "DownloadProgressBar", "letterbox"]
-
-
-def xywh2xyxy(x: np.ndarray):
-    y = np.copy(x)
-    y[..., 0] = x[..., 0] - x[..., 2] / 2  # top left x
-    y[..., 1] = x[..., 1] - x[..., 3] / 2  # top left y
-    y[..., 2] = x[..., 0] + x[..., 2] / 2  # bottom right x
-    y[..., 3] = x[..., 1] + x[..., 3] / 2  # bottom right y
-    return y
-
-
-def letterbox(
-    im: np.ndarray, new_shape: tuple = (640, 640), color: tuple = (114, 114, 114), auto: bool = False, stride: int = 32
-):
-    """Letterbox image transform for yolo models
-    Args:
-        im (np.ndarray): Input image
-        new_shape (tuple, optional): Image size. Defaults to (640, 640).
-        color (tuple, optional): Pixel fill value for the area outside the transformed image.
-        Defaults to (114, 114, 114).
-        auto (bool, optional): auto padding. Defaults to False.
-        stride (int, optional): padding stride. Defaults to 32.
-    Returns:
-        np.ndarray: Output image
-    """
-    # Resize and pad image while meeting stride-multiple constraints
-    im = np.array(im)
-    shape = im.shape[:2]  # current shape [height, width]
-    if isinstance(new_shape, int):
-        new_shape = (new_shape, new_shape)
-
-    # Scale ratio (new / old)
-    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
-
-    # Compute padding
-    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
-    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
-
-    if auto:  # minimum rectangle
-        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
-
-    dw /= 2  # divide padding into 2 sides
-    dh /= 2
-
-    if shape[::-1] != new_unpad:  # resize
-        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
-    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
-    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
-    # add border
-    h, w = im.shape[:2]
-    im_b = np.zeros((h + top + bottom, w + left + right, 3)) + color
-    im_b[top : top + h, left : left + w, :] = im
-
-    return im_b.astype("uint8"), (left, top)
+__all__ = ["nms", "DownloadProgressBar"]
 
 
 def box_iou(box1: np.ndarray, box2: np.ndarray, eps: float = 1e-7):
diff --git a/pyroengine/vision.py b/pyroengine/vision.py
index 9bac5dc8..17e54457 100644
--- a/pyroengine/vision.py
+++ b/pyroengine/vision.py
@@ -4,23 +4,29 @@
 # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
 
 import json
+import logging
 import os
-from typing import Optional, Tuple
+import platform
+import shutil
+from typing import Optional
 from urllib.request import urlretrieve
 
 import numpy as np
-import onnxruntime
 from huggingface_hub import HfApi  # type: ignore[import-untyped]
 from PIL import Image
+from ultralytics import YOLO  # type: ignore[import-untyped]
 
-from .utils import DownloadProgressBar, letterbox, nms, xywh2xyxy
+from .utils import DownloadProgressBar
 
 __all__ = ["Classifier"]
 
-MODEL_URL = "https://huggingface.co/pyronear/yolov8s/resolve/main/model.onnx"
+MODEL_URL_FOLDER = "https://huggingface.co/pyronear/yolov8s/resolve/main/"
 MODEL_ID = "pyronear/yolov8s"
-MODEL_NAME = "model.onnx"
-METADATA_PATH = "data/model_metadata.json"
+MODEL_NAME = "yolov8s.pt"
+METADATA_NAME = "model_metadata.json"
+
+
+logging.basicConfig(format="%(asctime)s | %(levelname)s: %(message)s", level=logging.INFO, force=True)
 
 
 # Utility function to save metadata
@@ -30,7 +36,7 @@ def save_metadata(metadata_path, metadata):
 
 
 class Classifier:
-    """Implements an image classification model using ONNX backend.
+    """Implements an image classification model using YOLO backend.
 
     Examples:
         >>> from pyroengine.vision import Classifier
@@ -40,56 +46,78 @@ class Classifier:
         model_path: model path
     """
 
-    def __init__(self, model_path: Optional[str] = "data/model.onnx", img_size: tuple = (640, 640)) -> None:
+    def __init__(self, model_folder="data", imgsz=1024, conf=0.15, iou=0.05, format="ncnn", model_path=None) -> None:
         if model_path is None:
-            model_path = "data/model.onnx"
-
-        # Get the expected SHA256 from Hugging Face
-        api = HfApi()
-        model_info = api.model_info(MODEL_ID, files_metadata=True)
-        expected_sha256 = self.get_sha(model_info.siblings)
-
-        if not expected_sha256:
-            raise ValueError("SHA256 hash for the model file not found in the Hugging Face model metadata.")
-
-        # Check if the model file exists
-        if os.path.isfile(model_path):
-            # Load existing metadata
-            metadata = self.load_metadata(METADATA_PATH)
-            if metadata and metadata.get("sha256") == expected_sha256:
-                print("Model already exists and the SHA256 hash matches. No download needed.")
+            if format == "ncnn":
+                if self.is_arm_architecture():
+                    model = "yolov8s_ncnn_model.zip"
+                else:
+                    logging.info("NCNN format is optimized for arm architecture only, switching to onnx")
+                    model = "yolov8s.onnx"
+            elif format in ["onnx", "pt"]:
+                model = f"yolov8s.{format}"
+
+            model_path = os.path.join(model_folder, model)
+            metadata_path = os.path.join(model_folder, METADATA_NAME)
+            model_url = MODEL_URL_FOLDER + model
+
+            # Get the expected SHA256 from Hugging Face
+            api = HfApi()
+            model_info = api.model_info(MODEL_ID, files_metadata=True)
+            expected_sha256 = self.get_sha(model_info.siblings)
+
+            if not expected_sha256:
+                raise ValueError("SHA256 hash for the model file not found in the Hugging Face model metadata.")
+
+            # Check if the model file exists
+            if os.path.isfile(model_path):
+                # Load existing metadata
+                metadata = self.load_metadata(metadata_path)
+                if metadata and metadata.get("sha256") == expected_sha256:
+                    logging.info("Model already exists and the SHA256 hash matches. No download needed.")
+                else:
+                    logging.info("Model exists but the SHA256 hash does not match or the file doesn't exist.")
+                    os.remove(model_path)
+                    self.download_model(model_url, model_path, expected_sha256, metadata_path)
             else:
-                print("Model exists but the SHA256 hash does not match or the file doesn't exist.")
-                os.remove(model_path)
-                self.download_model(model_path, expected_sha256)
-        else:
-            self.download_model(model_path, expected_sha256)
+                self.download_model(model_url, model_path, expected_sha256, metadata_path)
+
+            file_name, ext = os.path.splitext(model_path)
+            if ext == ".zip":
+                if not os.path.isdir(file_name):
+                    shutil.unpack_archive(model_path, model_folder)
+                model_path = file_name
+
+        self.model = YOLO(model_path, task="detect")
+        self.imgsz = imgsz
+        self.conf = conf
+        self.iou = iou
 
-        self.ort_session = onnxruntime.InferenceSession(model_path)
-        self.img_size = img_size
+    def is_arm_architecture(self):
+        # Check for ARM architecture
+        return platform.machine().startswith("arm") or platform.machine().startswith("aarch")
 
     def get_sha(self, siblings):
         # Extract the SHA256 hash from the model files metadata
         for file in siblings:
             if file.rfilename == os.path.basename(MODEL_NAME):
-                expected_sha256 = file.lfs.sha256
-                break
-        return expected_sha256
+                return file.lfs["sha256"]
+        return None
 
-    def download_model(self, model_path, expected_sha256):
+    def download_model(self, model_url, model_path, expected_sha256, metadata_path):
         # Ensure the directory exists
         os.makedirs(os.path.split(model_path)[0], exist_ok=True)
 
         # Download the model
-        print(f"Downloading model from {MODEL_URL} ...")
+        logging.info(f"Downloading model from {model_url} ...")
         with DownloadProgressBar(unit="B", unit_scale=True, miniters=1, desc=model_path) as t:
-            urlretrieve(MODEL_URL, model_path, reporthook=t.update_to)
-        print("Model downloaded!")
+            urlretrieve(model_url, model_path, reporthook=t.update_to)
+        logging.info("Model downloaded!")
 
         # Save the metadata
         metadata = {"sha256": expected_sha256}
-        save_metadata(METADATA_PATH, metadata)
-        print("Metadata saved!")
+        save_metadata(metadata_path, metadata)
+        logging.info("Metadata saved!")
 
     # Utility function to load metadata
     def load_metadata(self, metadata_path):
@@ -98,51 +126,14 @@ def load_metadata(self, metadata_path):
                 return json.load(f)
         return None
 
-    def preprocess_image(self, pil_img: Image.Image) -> Tuple[np.ndarray, Tuple[int, int]]:
-        """Preprocess an image for inference
-
-        Args:
-            pil_img: A valid PIL image.
-
-        Returns:
-            A tuple containing:
-            - The resized and normalized image of shape (1, C, H, W).
-            - Padding information as a tuple of integers (pad_height, pad_width).
-        """
-
-        np_img, pad = letterbox(np.array(pil_img), self.img_size)  # Applies letterbox resize with padding
-        np_img = np.expand_dims(np_img.astype("float"), axis=0)  # Add batch dimension
-        np_img = np.ascontiguousarray(np_img.transpose((0, 3, 1, 2)))  # Convert from BHWC to BCHW format
-        np_img = np_img.astype("float32") / 255  # Normalize to [0, 1]
+    def __call__(self, pil_img: Image.Image, occlusion_mask: Optional[np.ndarray] = None) -> np.ndarray:
 
-        return np_img, pad
+        results = self.model(pil_img, imgsz=self.imgsz, conf=self.conf, iou=self.iou)
+        y = np.concatenate(
+            (results[0].boxes.xyxyn.cpu().numpy(), results[0].boxes.conf.cpu().numpy().reshape((-1, 1))), axis=1
+        )
 
-    def __call__(self, pil_img: Image.Image, occlusion_mask: Optional[np.ndarray] = None) -> np.ndarray:
-        np_img, pad = self.preprocess_image(pil_img)
-
-        # ONNX inference
-        y = self.ort_session.run(["output0"], {"images": np_img})[0][0]
-        # Drop low conf for speed-up
-        y = y[:, y[-1, :] > 0.05]
-        # Post processing
-        y = np.transpose(y)
-        y = xywh2xyxy(y)
-        # Sort by confidence
-        y = y[y[:, 4].argsort()]
-        y = nms(y)
-        y = y[::-1]
-
-        # Normalize preds
-        if len(y) > 0:
-            # Remove padding
-            left_pad, top_pad = pad
-            y[:, :4:2] -= left_pad
-            y[:, 1:4:2] -= top_pad
-            y[:, :4:2] /= self.img_size[1] - 2 * left_pad
-            y[:, 1:4:2] /= self.img_size[0] - 2 * top_pad
-            y = np.clip(y, 0, 1)
-        else:
-            y = np.zeros((0, 5))  # normalize output
+        y = np.reshape(y, (-1, 5))
 
         # Remove prediction in occlusion mask
         if occlusion_mask is not None:
diff --git a/src/poetry.lock b/src/poetry.lock
index ec4c8e49..603f3428 100644
--- a/src/poetry.lock
+++ b/src/poetry.lock
@@ -138,6 +138,154 @@ humanfriendly = ">=9.1"
 [package.extras]
 cron = ["capturer (>=2.4)"]
 
+[[package]]
+name = "contourpy"
+version = "1.1.0"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "contourpy-1.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:89f06eff3ce2f4b3eb24c1055a26981bffe4e7264acd86f15b97e40530b794bc"},
+    {file = "contourpy-1.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dffcc2ddec1782dd2f2ce1ef16f070861af4fb78c69862ce0aab801495dda6a3"},
+    {file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:25ae46595e22f93592d39a7eac3d638cda552c3e1160255258b695f7b58e5655"},
+    {file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:17cfaf5ec9862bc93af1ec1f302457371c34e688fbd381f4035a06cd47324f48"},
+    {file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:18a64814ae7bce73925131381603fff0116e2df25230dfc80d6d690aa6e20b37"},
+    {file = "contourpy-1.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:90c81f22b4f572f8a2110b0b741bb64e5a6427e0a198b2cdc1fbaf85f352a3aa"},
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+    {file = "contourpy-1.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:1f795597073b09d631782e7245016a4323cf1cf0b4e06eef7ea6627e06a37ff2"},
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+]
+
+[package.dependencies]
+numpy = ">=1.16"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx-copybutton"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.2.0)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
+
+[[package]]
+name = "contourpy"
+version = "1.1.1"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.8"
+files = [
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@@ -280,6 +810,24 @@ docs = ["sphinx"]
 gmpy = ["gmpy2 (>=2.1.0a4)"]
 tests = ["pytest (>=4.6)"]
 
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@@ -581,6 +1392,20 @@ docs = ["Jinja2 (<3.1)", "furo (>=2022.3.4)", "sphinx (>=3.0.0,!=3.5.0)", "sphin
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+
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@@ -595,6 +1420,17 @@ files = [
 [package.extras]
 cli = ["click (>=5.0)"]
 
+[[package]]
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+description = "World timezone definitions, modern and historical"
+optional = false
+python-versions = "*"
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 version = "6.0.1"
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 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
 use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
 
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+python-versions = ">=3.8"
+files = [
+    {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"},
+    {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"},
+]
+
+[package.extras]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "f3a09ed219569aaca9e560ec4da8e73bc3a15209cdc1a69c65b9aeb6b15e9ef3"
+content-hash = "f38f4190eefbfccd9a4121d98312034dde72c64d8059d1cbd9efa077ad22481f"
\ No newline at end of file
diff --git a/src/pyproject.toml b/src/pyproject.toml
index fe3b3242..6d2fc3a4 100644
--- a/src/pyproject.toml
+++ b/src/pyproject.toml
@@ -14,4 +14,4 @@ python = "^3.8"
 pyroclient = { git = "https://github.com/pyronear/pyro-api.git", rev = "767be30a781b52b29d68579d543e3f45ac8c4713", subdirectory = "client" }
 pyroengine = "^0.2.0"
 python-dotenv = ">=0.15.0"
-opencv-python = "^4.5.5.64"
+ultralytics = "8.2.50"
\ No newline at end of file
diff --git a/src/requirements.txt b/src/requirements.txt
index 1065651b..ed72f00c 100644
--- a/src/requirements.txt
+++ b/src/requirements.txt
@@ -1,27 +1,55 @@
 certifi==2024.6.2 ; python_version >= "3.8" and python_version < "4"
 charset-normalizer==3.3.2 ; python_version >= "3.8" and python_version < "4"
-colorama==0.4.6 ; python_version >= "3.8" and python_version < "4" and platform_system == "Windows"
+colorama==0.4.6 ; python_version >= "3.8" and python_version < "4.0" and platform_system == "Windows"
 coloredlogs==15.0.1 ; python_version >= "3.8" and python_version < "4"
+contourpy==1.1.1 ; python_version >= "3.8" and python_version < "4.0"
+cycler==0.12.1 ; python_version >= "3.8" and python_version < "4.0"
 filelock==3.15.4 ; python_version >= "3.8" and python_version < "4"
 flatbuffers==24.3.25 ; python_version >= "3.8" and python_version < "4"
+fonttools==4.53.1 ; python_version >= "3.8" and python_version < "4.0"
 fsspec==2024.6.0 ; python_version >= "3.8" and python_version < "4"
 huggingface-hub==0.23.4 ; python_version >= "3.8" and python_version < "4"
 humanfriendly==10.0 ; python_version >= "3.8" and python_version < "4"
 idna==3.7 ; python_version >= "3.8" and python_version < "4"
+importlib-resources==6.4.0 ; python_version >= "3.8" and python_version < "3.10"
+intel-openmp==2021.4.0 ; python_version >= "3.8" and python_version < "4.0" and platform_system == "Windows"
+jinja2==3.1.4 ; python_version >= "3.8" and python_version < "4.0"
+kiwisolver==1.4.5 ; python_version >= "3.8" and python_version < "4.0"
+markupsafe==2.1.5 ; python_version >= "3.8" and python_version < "4.0"
+matplotlib==3.7.5 ; python_version >= "3.8" and python_version < "4.0"
+mkl==2021.4.0 ; python_version >= "3.8" and python_version < "4.0" and platform_system == "Windows"
 mpmath==1.3.0 ; python_version >= "3.8" and python_version < "4"
+networkx==3.1 ; python_version >= "3.8" and python_version < "4.0"
 numpy==1.24.4 ; python_version >= "3.8" and python_version < "4"
-onnxruntime==1.18.0 ; python_version >= "3.8" and python_version < "4"
+ncnn==1.0.20240410 ; python_version >= "3.8" and python_version < "4"
 opencv-python==4.10.0.84 ; python_version >= "3.8" and python_version < "4.0"
 packaging==24.1 ; python_version >= "3.8" and python_version < "4"
+pandas==2.0.3 ; python_version >= "3.8" and python_version < "4.0"
 pillow==10.3.0 ; python_version >= "3.8" and python_version < "4"
 protobuf==5.27.1 ; python_version >= "3.8" and python_version < "4"
+psutil==6.0.0 ; python_version >= "3.8" and python_version < "4.0"
+py-cpuinfo==9.0.0 ; python_version >= "3.8" and python_version < "4.0"
+pyparsing==3.1.2 ; python_version >= "3.8" and python_version < "4.0"
 pyreadline3==3.4.1 ; sys_platform == "win32" and python_version >= "3.8" and python_version < "4"
 pyroclient @ git+https://github.com/pyronear/pyro-api.git@767be30a781b52b29d68579d543e3f45ac8c4713#subdirectory=client ; python_version >= "3.8" and python_version < "4"
 pyroengine==0.2.0 ; python_version >= "3.8" and python_version < "4"
+python-dateutil==2.9.0.post0 ; python_version >= "3.8" and python_version < "4.0"
 python-dotenv==1.0.1 ; python_version >= "3.8" and python_version < "4.0"
-pyyaml==6.0.1 ; python_version >= "3.8" and python_version < "4"
+pytz==2024.1 ; python_version >= "3.8" and python_version < "4.0"
+pyyaml==6.0.1 ; python_version >= "3.8" and python_version < "4.0"
 requests==2.32.3 ; python_version >= "3.8" and python_version < "4"
+scipy==1.9.3 ; python_version >= "3.8" and python_version < "4.0"
+seaborn==0.13.2 ; python_version >= "3.8" and python_version < "4.0"
+six==1.16.0 ; python_version >= "3.8" and python_version < "4.0"
 sympy==1.12.1 ; python_version >= "3.8" and python_version < "4"
-tqdm==4.66.4 ; python_version >= "3.8" and python_version < "4"
+tbb==2021.13.0 ; python_version >= "3.8" and python_version < "4.0" and platform_system == "Windows"
+torch==2.3.1 ; python_version >= "3.8" and python_version < "4.0"
+torchvision==0.18.1 ; python_version >= "3.8" and python_version < "4.0"
+tqdm==4.66.4 ; python_version >= "3.8" and python_version < "4.0"
+triton==2.3.1 ; platform_system == "Linux" and platform_machine == "x86_64" and python_version < "3.12" and python_version >= "3.8"
 typing-extensions==4.12.2 ; python_version >= "3.8" and python_version < "4"
+tzdata==2024.1 ; python_version >= "3.8" and python_version < "4.0"
+ultralytics-thop==2.0.0 ; python_version >= "3.8" and python_version < "4.0"
+ultralytics==8.2.50 ; python_version >= "3.8" and python_version < "4.0"
 urllib3==2.2.2 ; python_version >= "3.8" and python_version < "4"
+zipp==3.19.2 ; python_version >= "3.8" and python_version < "3.10"
diff --git a/src/run.py b/src/run.py
index 64a1f121..f6951359 100644
--- a/src/run.py
+++ b/src/run.py
@@ -4,11 +4,10 @@
 # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.
 
 import argparse
+import asyncio
 import json
 import logging
 import os
-import time
-from pathlib import Path
 
 import urllib3
 from dotenv import load_dotenv
@@ -28,6 +27,7 @@ def main(args):
     # .env loading
     load_dotenv(".env")
     API_URL = os.environ.get("API_URL")
+    API_URL = "https://api.pyronear.org"
     LAT = float(os.environ.get("LAT"))
     LON = float(os.environ.get("LON"))
     assert isinstance(API_URL, str) and isinstance(LAT, float) and isinstance(LON, float)
@@ -52,13 +52,8 @@ def main(args):
 
         cameras.append(ReolinkCamera(_ip, CAM_USER, CAM_PWD, cam_data["type"], cam_poses, args.protocol))
 
-    # Check if model is available in cache
-    cache = Path(args.cache)
-
-    model_path = cache.joinpath("model.onnx") if args.model_path is None else args.model_path
-
     engine = Engine(
-        model_path,
+        args.model_path,
         args.thresh,
         API_URL,
         splitted_cam_creds,
@@ -80,11 +75,7 @@ def main(args):
         cameras,
     )
 
-    while True:
-        start_ts = time.time()
-        sys_controller.run(args.period)
-        # Sleep only once all images are processed
-        time.sleep(max(args.period - time.time() + start_ts, 0))
+    asyncio.run(sys_controller.main_loop(args.period))
 
 
 if __name__ == "__main__":
@@ -92,7 +83,7 @@ def main(args):
         description="Raspberry Pi system controller", formatter_class=argparse.ArgumentDefaultsHelpFormatter
     )
     # Model
-    parser.add_argument("--model_path", type=str, default="data/model.onnx", help="model path")
+    parser.add_argument("--model_path", type=str, default=None, help="model path")
     parser.add_argument("--thresh", type=float, default=0.15, help="Confidence threshold")
     # Camera & cache
     parser.add_argument("--creds", type=str, default="data/credentials.json", help="Camera credentials")
diff --git a/tests/test_core.py b/tests/test_core.py
index 20615db4..071fe8ab 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -1,13 +1,11 @@
-import time
+import asyncio
 from datetime import datetime
-from multiprocessing import Queue
-from unittest.mock import MagicMock, patch
+from unittest.mock import AsyncMock, MagicMock, patch
 
-import numpy as np
 import pytest
 from PIL import Image
 
-from pyroengine.core import SystemController, capture_camera_image, is_day_time
+from pyroengine.core import SystemController, is_day_time
 
 
 @pytest.fixture
@@ -47,11 +45,11 @@ def system_controller_ptz(mock_engine, mock_cameras_ptz):
 
 
 def test_is_day_time_ir_strategy(mock_wildfire_image):
-    # Use the mock_forest_stream image to simulate daylight image
+    # Use day image
     assert is_day_time(None, mock_wildfire_image, "ir")
 
-    # Create a black and white image to simulate night image
-    frame = Image.fromarray(np.zeros((100, 100, 3), dtype=np.uint8))
+    # Create a grayscale image to simulate night image
+    frame = Image.new("RGB", (100, 100), (255, 255, 255))
     assert not is_day_time(None, frame, "ir")
 
 
@@ -73,144 +71,62 @@ def test_is_day_time_time_strategy(tmp_path):
         assert not is_day_time(cache, None, "time")
 
 
-def test_capture_images(system_controller):
-    queue = Queue(maxsize=10)
-    for camera in system_controller.cameras:
-        capture_camera_image((camera, queue))
+@pytest.mark.asyncio
+async def test_capture_images(system_controller):
+    queue = asyncio.Queue(maxsize=10)
+    await system_controller.capture_images(queue)
 
     assert queue.qsize() == 1
-    cam_id, frame = queue.get(timeout=1)  # Use timeout to wait for the item
+    cam_id, frame = await queue.get()  # Use timeout to wait for the item
     assert cam_id == "192.168.1.1"
     assert isinstance(frame, Image.Image)
 
 
-def test_capture_images_ptz(system_controller_ptz):
-    queue = Queue(maxsize=10)
-    for camera in system_controller_ptz.cameras:
-        capture_camera_image((camera, queue))
-
-    # Retry logic to account for potential timing issues
-    retries = 10
-    while retries > 0 and queue.qsize() < 2:
-        time.sleep(0.1)
-        retries -= 1
+@pytest.mark.asyncio
+async def test_capture_images_ptz(system_controller_ptz):
+    queue = asyncio.Queue(maxsize=10)
+    await system_controller_ptz.capture_images(queue)
 
     assert queue.qsize() == 2
-    cam_id, frame = queue.get(timeout=1)  # Use timeout to wait for the item
+    cam_id, frame = await queue.get()  # Use timeout to wait for the item
     assert cam_id == "192.168.1.1_1"
     assert isinstance(frame, Image.Image)
 
 
-def test_analyze_stream(system_controller):
+@pytest.mark.asyncio
+async def test_analyze_stream(system_controller):
+    queue = asyncio.Queue()
     mock_frame = Image.new("RGB", (100, 100))
-    cam_id = "192.168.1.1"
-    system_controller.analyze_stream(mock_frame, cam_id)
-    system_controller.engine.predict.assert_called_once_with(mock_frame, cam_id)
-
-
-def test_run(system_controller):
-    with patch.object(system_controller, "capture_images", return_value=Queue()), patch.object(
-        system_controller, "analyze_stream"
-    ), patch.object(system_controller.engine, "_process_alerts"), patch("signal.signal"), patch("signal.alarm"), patch(
-        "time.sleep", side_effect=InterruptedError
-    ):  # Mock sleep to exit the loop
-
-        try:
-            system_controller.run(period=2)
-        except InterruptedError:
-            pass
-
-
-def test_run_no_images(system_controller):
-    with patch.object(system_controller, "capture_images", return_value=Queue()), patch.object(
-        system_controller, "analyze_stream"
-    ) as mock_analyze_stream, patch.object(system_controller.engine, "_process_alerts"), patch("signal.signal"), patch(
-        "signal.alarm"
-    ), patch(
-        "time.sleep", side_effect=InterruptedError
-    ):  # Mock sleep to exit the loop
-
-        try:
-            system_controller.run(period=2)
-        except InterruptedError:
-            pass
-
-        mock_analyze_stream.assert_not_called()
-
-
-def test_run_capture_exception(system_controller):
-    with patch.object(system_controller, "capture_images", side_effect=Exception("Capture error")), patch.object(
-        system_controller.engine, "_process_alerts"
-    ), patch("signal.signal"), patch("signal.alarm"), patch(
-        "time.sleep", side_effect=InterruptedError
-    ):  # Mock sleep to exit the loop
-
-        try:
-            system_controller.run(period=2)
-        except InterruptedError:
-            pass
-
-
-def test_capture_camera_image_exception():
-    queue = Queue(maxsize=10)
-    camera = MagicMock()
-    camera.cam_type = "static"
-    camera.ip_address = "192.168.1.1"
-    camera.capture.side_effect = Exception("Capture error")
-
-    capture_camera_image((camera, queue))
-
-    assert queue.qsize() == 0
-
-
-def test_repr_method(system_controller):
-    repr_str = repr(system_controller)
-    # Check if the representation is a string
-    assert isinstance(repr_str, str)
-
-
-def test_repr_method_no_cameras(mock_engine):
-    system_controller = SystemController(engine=mock_engine, cameras=[])
-    repr_str = repr(system_controller)
-    # Check if the representation is a string
-    assert isinstance(repr_str, str)
+    await queue.put(("192.168.1.1", mock_frame))
 
+    analyze_task = asyncio.create_task(system_controller.analyze_stream(queue))
+    await queue.put(None)  # Signal the end of the stream
+    await analyze_task
 
-def test_capture_camera_image():
-    queue = Queue(maxsize=10)
-    camera = MagicMock()
-    camera.cam_type = "static"
-    camera.ip_address = "192.168.1.1"
-    camera.capture.return_value = Image.new("RGB", (100, 100))  # Mock captured image
+    system_controller.engine.predict.assert_called_once_with(mock_frame, "192.168.1.1")
 
-    capture_camera_image((camera, queue))
 
-    assert queue.qsize() == 1
-    cam_id, frame = queue.get(timeout=1)  # Use timeout to wait for the item
-    assert cam_id == "192.168.1.1"
-    assert isinstance(frame, Image.Image)
+@pytest.mark.asyncio
+async def test_capture_images_method(system_controller):
+    with patch("pyroengine.core.capture_camera_image", new_callable=AsyncMock) as mock_capture:
+        queue = asyncio.Queue()
+        await system_controller.capture_images(queue)
 
+        for camera in system_controller.cameras:
+            mock_capture.assert_any_call(camera, queue)
+        assert mock_capture.call_count == len(system_controller.cameras)
 
-def test_capture_camera_image_ptz():
-    queue = Queue(maxsize=10)
-    camera = MagicMock()
-    camera.cam_type = "ptz"
-    camera.cam_poses = [1, 2]
-    camera.ip_address = "192.168.1.1"
-    camera.capture.return_value = Image.new("RGB", (100, 100))  # Mock captured image
 
-    capture_camera_image((camera, queue))
+@pytest.mark.asyncio
+async def test_analyze_stream_method(system_controller):
+    queue = asyncio.Queue()
+    mock_frame = Image.new("RGB", (100, 100))
+    await queue.put(("192.168.1.1", mock_frame))
+    await queue.put(None)  # Signal the end of the stream
 
-    # Retry logic to account for potential timing issues
-    retries = 10
-    while retries > 0 and queue.qsize() < 2:
-        time.sleep(0.1)
-        retries -= 1
+    await system_controller.analyze_stream(queue)
 
-    assert queue.qsize() == 2
-    cam_id, frame = queue.get(timeout=1)  # Use timeout to wait for the item
-    assert cam_id == "192.168.1.1_1"
-    assert isinstance(frame, Image.Image)
+    system_controller.engine.predict.assert_called_once_with(mock_frame, "192.168.1.1")
 
 
 def test_check_day_time(system_controller):
@@ -230,3 +146,16 @@ def test_check_day_time(system_controller):
         system_controller.check_day_time()
         mock_is_day_time.assert_called_once()
         mock_logging_exception.assert_called_once_with("Exception during initial day time check: Error in is_day_time")
+
+
+def test_repr_method(system_controller):
+    repr_str = repr(system_controller)
+    # Check if the representation is a string
+    assert isinstance(repr_str, str)
+
+
+def test_repr_method_no_cameras(mock_engine):
+    system_controller = SystemController(engine=mock_engine, cameras=[])
+    repr_str = repr(system_controller)
+    # Check if the representation is a string
+    assert isinstance(repr_str, str)
diff --git a/tests/test_engine.py b/tests/test_engine.py
index 1f2a48ee..9df30146 100644
--- a/tests/test_engine.py
+++ b/tests/test_engine.py
@@ -92,7 +92,6 @@ def test_engine_online(tmpdir_factory, mock_wildfire_stream, mock_wildfire_image
 
     if isinstance(api_url, str):
         engine = Engine(
-            folder + "model.onnx",
             api_url=api_url,
             cam_creds=cam_creds,
             latitude=float(lat),
diff --git a/tests/test_sensors.py b/tests/test_sensors.py
index f29679ea..d3d6dda8 100644
--- a/tests/test_sensors.py
+++ b/tests/test_sensors.py
@@ -127,3 +127,58 @@ def test_delete_ptz_preset_failure():
         camera.delete_ptz_preset(idx=1)
         # Assert that a failed operation logs an error message
         assert mock_response.json.call_count == 1
+
+
+def test_reboot_camera_success():
+    # Mock the response of requests.post to return a successful response for reboot
+    mock_response = MagicMock()
+    mock_response.status_code = 200
+    mock_response.json.return_value = [{"code": 0}]
+
+    with patch("requests.post", return_value=mock_response):
+        camera = ReolinkCamera("192.168.1.1", "login", "pwd", "static")
+        response = camera.reboot_camera()
+        # Assert that the reboot_camera method was called successfully
+        assert mock_response.json.call_count == 1
+        assert response == mock_response.json.return_value
+
+
+def test_get_auto_focus_success():
+    # Mock the response of requests.post to return a successful response with autofocus data
+    mock_response = MagicMock()
+    mock_response.status_code = 200
+    mock_response.json.return_value = [{"code": 0, "value": {"AutoFocus": [{"channel": 0, "disable": 0}]}}]
+
+    with patch("requests.post", return_value=mock_response):
+        camera = ReolinkCamera("192.168.1.1", "login", "pwd", "static")
+        response = camera.get_auto_focus()
+        # Assert that the get_auto_focus method returns the correct data
+        assert response == mock_response.json.return_value
+
+
+def test_set_auto_focus_success():
+    # Mock the response of requests.post to return a successful response for setting autofocus
+    mock_response = MagicMock()
+    mock_response.status_code = 200
+    mock_response.json.return_value = [{"code": 0}]
+
+    with patch("requests.post", return_value=mock_response):
+        camera = ReolinkCamera("192.168.1.1", "login", "pwd", "static")
+        response = camera.set_auto_focus(disable=True)
+        # Assert that the set_auto_focus method was called successfully
+        assert mock_response.json.call_count == 1
+        assert response == mock_response.json.return_value
+
+
+def test_start_zoom_focus_success():
+    # Mock the response of requests.post to return a successful response for starting zoom focus
+    mock_response = MagicMock()
+    mock_response.status_code = 200
+    mock_response.json.return_value = [{"code": 0}]
+
+    with patch("requests.post", return_value=mock_response):
+        camera = ReolinkCamera("192.168.1.1", "login", "pwd", "ptz")
+        response = camera.start_zoom_focus(position=100)
+        # Assert that the start_zoom_focus method was called successfully
+        assert mock_response.json.call_count == 1
+        assert response == mock_response.json.return_value
diff --git a/tests/test_vision.py b/tests/test_vision.py
index 98a378ff..7e788ced 100644
--- a/tests/test_vision.py
+++ b/tests/test_vision.py
@@ -1,108 +1,92 @@
+import datetime
 import os
-from unittest.mock import MagicMock, patch
+from unittest.mock import patch
 
 import numpy as np
-import pytest
 
 from pyroengine.vision import Classifier
 
-METADATA_PATH = "data/model_metadata.json"
-model_path = "data/model.onnx"
-sha = "9f1b1c2654d98bbed91e514ce20ea73a0a5fbd1111880f230d516ed40ea2dc58"
 
+def get_creation_date(file_path):
+    if os.path.exists(file_path):
 
-def custom_isfile_false(path):
-    if path == model_path:
-        return False  # or True based on your test case
-    return True  # Default behavior for other paths
+        # For Unix-like systems
+        stat = os.stat(file_path)
+        try:
+            creation_time = stat.st_birthtime
+        except AttributeError:
+            # On Unix, use the last modification time as a fallback
+            creation_time = stat.st_mtime
 
+        creation_date = datetime.datetime.fromtimestamp(creation_time)
+        return creation_date
+    else:
+        return None
 
-def custom_isfile_true(path):
-    if path == model_path:
-        return True  # or True based on your test case
-    return True  # Default behavior for other paths
 
-
-# Test for the case : the model doesn't exist
-def test_classifier(mock_wildfire_image):
+def test_classifier(tmpdir_factory, mock_wildfire_image):
     print("test_classifier")
-    with patch("os.path.isfile", side_effect=custom_isfile_false):
-        # Instantiate the ONNX model
-        model = Classifier()
-        # Check preprocessing
-        out, pad = model.preprocess_image(mock_wildfire_image)
-        assert isinstance(out, np.ndarray) and out.dtype == np.float32
-        assert out.shape == (1, 3, 640, 640)
-        assert isinstance(pad, tuple)
-        # Check inference
-        out = model(mock_wildfire_image)
-        assert out.shape == (1, 5)
-        conf = np.max(out[:, 4])
-        assert conf >= 0 and conf <= 1
-
-        # Test mask
-        mask = np.ones((384, 640))
-        out = model(mock_wildfire_image, mask)
-        print(out)
-        assert out.shape == (1, 5)
-
-        mask = np.zeros((384, 640))
-        out = model(mock_wildfire_image, mask)
-        print(out)
-        assert out.shape == (0, 5)
-        os.remove(model_path)
-        os.remove(METADATA_PATH)
-
-
-# Test that the model is not loaded
-def test_no_download():
-    print("test_no_download")
-    data = {"sha256": sha}
-    with patch("os.path.isfile", side_effect=custom_isfile_true):
-        with patch("pyroengine.vision.Classifier.load_metadata", return_value=data):
-            with patch("onnxruntime.InferenceSession", return_value=None):
-                Classifier()
-    assert os.path.isfile(model_path) is False
-
-
-# Test if sha are not the same
-@patch("pyroengine.vision.urlretrieve")
-@patch("pyroengine.vision.DownloadProgressBar")
-def test_sha_inequality(mock_download_progress, mock_urlretrieve):
-    print("test_sha_inequality")
-    data = {"sha256": "falsesha"}
-
-    # Mock urlretrieve to create a fake file
-    def fake_urlretrieve(url, filename, reporthook=None):
-        with open(filename, "w") as f:
-            f.write("fake model content")
-
-    mock_urlretrieve.side_effect = fake_urlretrieve
-    # Mock the DownloadProgressBar context manager
-    mock_progress_bar_instance = MagicMock()
-    mock_download_progress.return_value.__enter__.return_value = mock_progress_bar_instance
-
-    with patch("os.path.isfile", side_effect=custom_isfile_true):
-        with patch("pyroengine.vision.Classifier.load_metadata", return_value=data):
-            with patch(
-                "pyroengine.vision.Classifier.get_sha",
-                return_value=sha,
-            ):
-                with patch("onnxruntime.InferenceSession", return_value=None):
-                    with patch("os.remove", return_value=True):
-                        model = Classifier()
-
-    assert os.path.isfile(model_path) is True
-    assert model.load_metadata("non_existent_metadata.json") is None
-    os.remove(model_path)
-    os.remove(METADATA_PATH)
+    folder = str(tmpdir_factory.mktemp("engine_cache"))
+
+    # Instantiate the ONNX model
+    model = Classifier(model_folder=folder)
+    # Check inference
+    out = model(mock_wildfire_image)
+    assert out.shape[1] == 5
+    conf = np.max(out[:, 4])
+    assert 0 <= conf <= 1
+
+    # Test onnx model
+    model = Classifier(model_folder=folder, format="onnx")
+    model_path = os.path.join(folder, "yolov8s.onnx")
+    assert os.path.isfile(model_path)
+
+    # Test mask
+    mask = np.ones((384, 640))
+    out = model(mock_wildfire_image, mask)
+    assert out.shape == (1, 5)
+
+    mask = np.zeros((384, 640))
+    out = model(mock_wildfire_image, mask)
+    assert out.shape == (0, 5)
+
+    # Test dl pt model
+    _ = Classifier(model_folder=folder, format="pt")
+    model_path = os.path.join(folder, "yolov8s.pt")
+    assert os.path.isfile(model_path)
+
+    # Test dl ncnn model
+    with patch.object(Classifier, "is_arm_architecture", return_value=True):
+        _ = Classifier(model_folder=folder)
+        model_path = os.path.join(folder, "yolov8s_ncnn_model")
+        assert os.path.isdir(model_path)
+
+
+def test_download(tmpdir_factory):
+    print("test_classifier")
+    folder = str(tmpdir_factory.mktemp("engine_cache"))
 
+    # Instantiate the ONNX model
+    _ = Classifier(model_folder=folder)
 
-# Test for raising ValueError if expected_sha256 is not found
-def test_raise_value_error_if_no_sha256():
-    print("test_raise_value_error_if_no_sha256")
-    with patch("pyroengine.vision.Classifier.get_sha", return_value=""):
-        with pytest.raises(
-            ValueError, match="SHA256 hash for the model file not found in the Hugging Face model metadata."
-        ):
-            Classifier(model_path="non_existent_model.onnx")
+    model_path = os.path.join(folder, "yolov8s.onnx")
+    model_creation_date = get_creation_date(model_path)
+
+    # No download if exist
+    _ = Classifier(model_folder=folder)
+    model_creation_date2 = get_creation_date(model_path)
+    assert model_creation_date == model_creation_date2
+
+    # Download if does not exist
+    os.remove(model_path)
+    _ = Classifier(model_folder=folder)
+    model_creation_date3 = get_creation_date(model_path)
+    print(model_creation_date, model_creation_date3)
+    assert model_creation_date != model_creation_date3
+
+    # Download if sha is not the same
+    with patch.object(Classifier, "get_sha", return_value="sha12"):
+        _ = Classifier(model_folder=folder)
+        model_creation_date4 = get_creation_date(model_path)
+        print(model_creation_date, model_creation_date3)
+        assert model_creation_date4 != model_creation_date3