SRLane+: Improved Sketch and Refinement Method for Lane Detection

Abstract

Lane detection is a significant task with applications in autonomous driving tasks such as adaptive cruise control, lane departure warning, and lane-keep assistance. Convolutional neural networks (CNN) and transformers have been used for lane detection and achieved great performance. However, there are still challenges under complex scenarios, such as crowded or dazzling conditions. Utilizing the global and slender shape of lanes, line anchor-based methods have attracted much attention. Sketch and refinement (SRLane) is a two-stage anchor-based method for lane detection, composed of proposal generation stage (Sketch) and Refinement stage. This paper improves SRLane on both stages. At the first stage, position embedding is introduced and fused with the multi-scale feature maps to estimate local direction map more accurately for anchor proposal generation. At the second stage, a new fine regressor is proposed, which includes a multi-scale feature fusion module and a transformer decoder to further refine the lanes utilizing the context information. The whole method adopts an end-to-end training mechanism. Experiments are conducted on the CULane and Tusimple datasets. The results show that the proposed method achieves 80.35\% F1 score on the CULane dataset, outperforming existing methods.