BANet: Bidirectional Feature Aggregation and Adaptive Multi-Scene Perception-Based Lane Detection for Autonomous Driving

Lane detection is a hot topic in the field of autonomous driving, providing essential assistance to vehicles. Due to the lack of effective integration among hierarchical features and insufficient adaptability to variations of lanes under diverse background conditions, accurate lane detection remains challenging. To address the aforementioned issues, we propose BANet, an efficient lane detection network based on bidirectional feature aggregation and adaptive multi-scene perception, aiming to improve the lane detection accuracy under different backgrounds. Firstly, we propose a Bidirectional Feature Aggregation Module (BFAM) that, via the proposed composition built from PG2f, effectively integrates complementary information across scales, improving anchor localization. Secondly, we propose an Adaptive Multi-scene Perception Module (AMPM) that learns scene-aware spatial information to enhance lane-relevant cues, addressing the no-visual-clue problem. Finally, we propose an Edge Refinement Attention (ERA), which models spatial and channel information in parallel to refine lane representations. The experimental results on the CULane, CurveLanes, and TuSimple datasets show that the proposed network outperforms existing methods and exhibits excellent performance in the most challenging scenes.