A Topology-Aware Lane Instance Segmentation Network Based on Dynamic Snake Convolution and High-Resolution Feature Pyramid

Accurate lane detection is essential for autonomous driving, but the elongated geometry of lane markings and the information loss caused by perspective projection still make continuous lane perception challenging. Existing Convolutional Neural Network (CNN) methods are limited by fixed receptive fields and repeated downsampling, which can weaken narrow curvilinear structures and distant lane details. To address these issues, this paper proposes an enhanced single-stage lane instance segmentation framework based on YOLOv11. Dynamic Snake Convolution (DSConv) is integrated into the feature extraction network as an adaptive sampling operator for finite-width lane-mask representations, enabling local sampling trajectories to better follow narrow curved lane structures. A high-resolution P2 branch (Stride = 4) is further constructed in the Feature Pyramid Network (FPN) to compensate for fine-grained spatial details lost in deep feature maps. A reproducible point-to-mask training and mask-to-point evaluation pipeline is also established for the sparse TuSimple annotations. Experiments on the TuSimple benchmark, which mainly represents structured daytime highway scenes, show that the proposed method improves mask Precision, Recall, and mAP50 by 8.5, 9.0, and 6.8 percentage points, respectively, compared with YOLOv11-seg, while reaching 103.09 FPS under the reported testing configuration. Qualitative results on a newly constructed CULane subset further indicate improved lane continuity under reflections, illumination changes, night scenes, and complex urban backgrounds, while failure cases reveal remaining sensitivity to lane-like linear structures and weak lane visibility. The results suggest a practical accuracy–efficiency trade-off within the YOLO-style segmentation framework, although robustness in broader real-world scenarios still requires further verification.