ACL-Net: A Lane Detection Method Based on Coordinate Attention and Multi-Scale Context Enhancement

Lane detection is a crucial perception task for autonomous driving, but existing methods often struggle with spatial information loss, feature upsampling artifacts, and prediction discontinuities under complex scenarios such as occlusions or poor lighting. To address these limitations, this paper proposes ACL-Net, an end-to-end lane detection network integrating attention mechanisms and context enhancement based on the Cross Layer Refinement Network framework. First, a coordinate attention module is embedded at the output of the backbone network to recalibrate spatial position information and mitigate depth-induced detail loss. Second, the feature pyramid network is reconstructed utilizing a dynamic upsampling operator and an additional bottom-up pathway to prevent edge distortion and preserve fine-grained geometric features. Finally, a lane-aware atrous spatial pyramid pooling module with asymmetric convolutions is designed to aggregate multi-scale global context, effectively reconnecting fragmented lane lines caused by visual occlusions. Extensive experiments on the TuSimple and CULane datasets demonstrate the superiority of the proposed approach. ACL-Net achieves an accuracy of 96.98% on TuSimple and a total F1-measure of 80.34% on CULane, outperforming the baseline Cross Layer Refinement Network while maintaining a real-time inference speed of 61.90 FPS. The results indicate that ACL-Net significantly improves the utilization of geometric features and exhibits enhanced robustness in challenging road conditions, including severe occlusions, nighttime, and large-curvature curves.