CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching Process

TL;DR

CLRmatchNet introduces a deep learning-based label assignment module integrated into lane detection networks, significantly improving curved lane detection accuracy and confidence levels over traditional methods.

Contribution

The paper presents MatchNet, a novel deep learning submodule that replaces classical label assignment, enhancing curved lane detection performance in CLRNet-based models.

Findings

Achieved over 2.8% accuracy improvement on ResNet34 backbone.

Enhanced detection performance on curved lanes.

Maintained or improved results across different backbone architectures.

Abstract

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. Accurate label assignment has great impact on the model performance, that is usually relying on a pre-defined classical cost function evaluating GT-prediction alignment. However, classical label assignment methods face limitations due to their reliance on predefined cost functions derived from low-dimensional models, potentially impacting their optimality. Our research introduces MatchNet, a deep learning submodule-based approach aimed at improving the label assignment process. Integrated into a state-of-the-art lane detection network such as the Cross Layer Refinement Network for Lane Detection (CLRNet), MatchNet replaces the conventional label assignment process with a submodule network. The integrated model, CLRmatchNet, surpasses CLRNet, showing substantial 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. Our code is available at: https://github.com/sapirkontente/CLRmatchNet.git