FLAMNet: A Flexible Line Anchor Mechanism Network for Lane Detection

Lane detection is critical for intelligent vehicles to sense drivable areas. Compared to general objects, lane lines are slender-shaped, easily occluded, or defaced. Therefore, the lane detection network requires a more robust ability for local detail extraction and global semantic information modeling. In this paper, we propose a novel lane detection network (FLAMNet) with a flexible line anchor mechanism, which constantly corrects the position of line anchors to improve detection performance and computational efficiency. Specifically, we utilize the Patch Pooling Aggregation Module (PPAM) to aggregate multi-scale semantic features extracted by the backbone network. The multi-scale features are subsequently inputted into DSAformer, which utilizes decomposed self-attention to establish global long-distance dependencies. The detection head leverages fused features of multi-scale global and local details to accurately fit the lane line by correcting the anchor position. Moreover, we propose the Horizontal Information Aggregation Module (HIAM) to expand the receptive field of line anchors horizontally, enhancing the line anchor representation ability to the topological structure of complex lane lines. The experimental results on mainstream lane detection benchmark datasets demonstrate that the proposed FLAMNet outperforms existing methods. We have uploaded the code and demo of FLAMNet on GitHub at: https://github.com/RanHao-cq/FLAMNet .