DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation

Overview

This project presents DABNet (Depth-wise Asymmetric Bottleneck), a lightweight architecture designed for real-time semantic segmentation tasks. DABNet leverages depth-wise convolutional layers and asymmetric bottlenecks to achieve a balance between model complexity and segmentation accuracy, making it suitable for deployment on resource-constrained devices.

Key Features

• Lightweight architecture optimized for real-time performance.
• Depth-wise convolutional layers for efficient feature extraction.
• Asymmetric bottlenecks for balancing model complexity and accuracy.
• Supports semantic segmentation of outdoor scenes with diverse environments.

Dependencies

Ensure you have the following dependencies installed:

• Python (>=3.6)
• TensorFlow (>=2.0)
• OpenCV
• NumPy
• Matplotlib

You can install the dependencies using pip:

pip install tensorflow opencv-python numpy matplotlib

Dataset

You'll need a dataset of outdoor scenes annotated with semantic segmentation labels.

Click here to view the dataset used here

Conclusion

DABNet offers a compelling solution for real-time semantic segmentation of outdoor scenes, catering to the growing demand for efficient computer vision algorithms in resource-constrained environments. By leveraging depth-wise convolutional layers and asymmetric bottlenecks, DABNet strikes a balance between model complexity and segmentation accuracy, paving the way for diverse applications in autonomous systems and environmental analysis.