Intersection lane line segmentation algorithm based on improved YOLOv8n-seg

Lane line segmentation technology is crucial for extracting traffic parameters at urban road intersections. Currently, manually delineating intersection lane lines can no longer meet the demands of constructing digital traffic systems. Due to significant environmental interference and the diverse distribution of lane lines in the perspective of intersection video surveillance, existing lane line segmentation algorithms are ineffective for intersection scenarios. To address this issue, we propose an improved intersection lane line segmentation model based on YOLOv8n-seg. Firstly, inspired by the architecture of the Vision Transformer (ViT), we optimize the C2f module and incorporate attention mechanisms to enhance the network's ability to integrate features and capture global information. Secondly, we replace strided convolutions with a Haar wavelet downsampling module, which preserves all feature map information during downsampling. The experimental results show that [email protected] and [email protected]:0.95 of the improved YOLOv8n-seg in segmentation are 5.7% and 1.5 % higher than the original model, and the advantages of low parameters and high FPS are maintained, which can effectively realize segmenting intersection lane lines.