PolyLaneNet provides code for deep polynomial regression-based lane estimation, targeting researchers and engineers in autonomous driving and computer vision. It offers a novel approach to lane detection by modeling lanes as polynomial curves, enabling robust estimation even in challenging scenarios.

How It Works

The core of PolyLaneNet is its deep neural network architecture, which predicts polynomial coefficients and confidence scores for each lane. This approach allows for a continuous representation of lanes, offering advantages over traditional segmentation methods by handling lane curvature and fragmentation more effectively. The model is trained using a custom loss function that balances confidence prediction and polynomial regression accuracy.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt
• Requires Python 3.5.2+
• Training and testing scripts are provided.
• Configuration is managed via YAML files.
• Official models and dataset details (TuSimple, ELAS, LLAMAS) are available.

Highlighted Details

• State-of-the-art performance on lane detection benchmarks.
• Polynomial regression for continuous lane representation.
• Customizable training configurations via YAML.
• Supports multiple datasets including TuSimple, ELAS, and LLAMAS.

Maintenance & Community

The project is associated with the ICPR 2020 paper "PolyLaneNet: Lane Estimation via Deep Polynomial Regression." The authors have also released a new method, LaneATT. No specific community channels (Discord, Slack) are mentioned.

Licensing & Compatibility

The repository does not explicitly state a license. It is presented as code for a research paper, implying potential restrictions on commercial use. Users should verify licensing before deployment.

Limitations & Caveats

The code requires specific Python versions and dependencies. Reproducing exact paper results may necessitate downloading pre-trained models and carefully configuring paths. The validate option during training is noted as not thoroughly tested.