CLASPPNet: A Cross-Layer Multi-class Lane Semantic Segmentation Model Fused with Lane Detection Module

Multi-class lane semantic segmentation is a crucial technology in the traffic violation detection system. However, the existing models for multi-classification lane semantic segmentation suffer from low segmentation accuracy for special lanes (e.g., ramp, emergency lane) and lane lines. To address this problem, we propose a cross-layer multi-class lane semantic segmentation model called CLASPPNet (Cross-Layer Atrous Spatial Pyramid Pooling Network) fused with lane detection module. We first design a Cross-Layer Atrous Spatial Pyramid Pooling (CLASPP) structure to integrate the deep and shallow features in the image and enhance the integrity of the lane segmentation. Additionally, we integrate the lane detection module during training in the cross-layer structure, which can improve the model’s ability of extracting lane line features. We evaluate CLASPPNet on the expressway dataset based on aerial view, and the experimental results show that our model significantly improves the segmentation performance of special lanes and lane lines. Additionally, it achieves the highest mIoU (mean Intersection over Union) of 86.4% while having 28.9M parameters.