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onnx-Ultra-Fast-Lane-Detection-Inference With Nvidia Xavier

Example scripts for the detection of lanes using the ultra fast lane detection model in ONNX.

!Ultra fast lane detection Source: https://www.flickr.com/photos/32413914@N00/1475776461/

Pytorch inference

For performing the inference in Pytorch, check my other repository Ultrafast Lane Detection Inference Pytorch.

Requirements

  • OpenCV, scipy, onnx and onnxruntime. pafy and youtube-dl, Nvidia Xavier AGX, JetPack 4.6 and Python3.6 are required for youtube video inference.

Installation

pip3 install opencv-python
pip3 install scipy
Download >> https://nvidia.app.box.com/s/bfs688apyvor4eo8sf3y1oqtnarwafww
Install  >> pip3 install onnxruntime_gpu-1.8.0-cp36-cp36m-linux_aarch64.whl
pip3 install scikit-build
if you found not match version try to upgrade the PIP >> sudo -H pip3 install --upgrade pip

ONNX model

The original model was converted to different formats (including .onnx) by PINTO0309, download the models from his repository and save it into the models folder.

ONNX Conversion script: cfzd/Ultra-Fast-Lane-Detection#218

Original Pytorch model

The pretrained Pytorch model was taken from the original repository.

Model info (link)

  • Input: RGB image of size 800 x 200 pixels.
  • Output: Keypoints for a maximum of 4 lanes (left-most lane, left lane, right lane, and right-most lane).

Examples

  • Image inference:
python imageLaneDetection.py
  • Webcam inference:
python webcamLaneDetection.py
  • Video inference:
python videoLaneDetection.py

Result

Original video: https://youtu.be/2CIxM7x-Clc (by Yunfei Guo)