CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process

Lane detection plays a crucial role in autonomous driving by providing vital data to ensure safe navigation. Modern algorithms rely on anchor-based detectors, which are then followed by a label assignment process to categorize training detections as positive or negative instances based on learned geometric attributes. The current methods, however, have limitations and might not be optimal since they rely on predefined classical cost functions that are based on a low-dimensional model. Our research introduces MatchNet, a deep learning sub-module-based approach aimed at enhancing the label assignment process. Integrated into a state-of-the-art lane detection network like the Cross Layer Refinement Network for Lane Detection (CLRNet), MatchNet replaces the conventional label assignment process with a sub-module network. This integration results in significant improvements in scenarios involving curved lanes, with remarkable improvement across all backbones of +2.8% for ResNet34, +2.3% for ResNet101, and +2.96% for DLA34. In addition, it maintains or even improves comparable results in other sections. Our method boosts the confidence level in lane detection, allowing an increase in the confidence threshold. The code will be available soon: https://github.com/sapirkontente/CLRmatchNet.git