DiffLane: Diffusion Model-Based Lane Mask Generation for Accurate Video Lane Detection

Mask-based video lane detection methods currently have achieved promising performance. However, they generate irregular lane masks in complex scenes, resulting in inaccurate lane positioning. Diffusion models have achieved notable success in the field of image segmentation because of their ability to restore pixel-level details. In this paper, we propose a novel framework DiffLane, termed Diffusion Model-Based Lane Mask Generation for Accurate Video Lane Detection. The main idea of our work is to exploit the detail-restoring capability of diffusion models to generate high-quality lane masks. DiffLane includes the MultiFrame Fusion Enhancer (MFFE), the MultiScale De-noising Network (MSDN) and the Dynamic Lane Perception Unit (DLPU). In MFFE, the current frame is enhanced with visual information from the past two frames through global matching-based optical flow estimation. This enhanced frame serves as a condition for each denoising step. MSDN predicts noise through a multi-scale fusion strategy, enabling the diffusion model to remove noise and generate regular lane masks precisely. DLPU regresses the coefficient vectors from the generated lane masks with DSConv applied in two directions, completing the accurate video lane detection task. Extensive experiments on the VIL-100 and OpenLane-V datasets demonstrate that our method outperforms other state-of-the-art approaches.