Aiming at the problems of slow detection speed and low detection accuracy in existing lane detection methods,lane de-tection is regarded as a classification problem,and a lane detection method based on RepVGG is proposed.Based on the RepVGG model,different levels of feature maps are fused in the backbone network to reduce the loss of spatial positioning information and improve the accuracy of lane positioning.Modeling lane as a whole and correcting lane line prediction effects from both overall and local perspectives through post-processing.Introducing a branch of lane presence prediction based on distribution guidance to learn the lane presence features directly from the localization distribution,working in conjunction with post-processing to further improve the detection accuracy while enhancing the inference speed.Experiments on the TuSimple dataset and the CULane data-set show that the proposed method achieves a good balance in speed and accuracy.On the CULane dataset,the reasoning speed is 1.13 times faster than UFLDv2 and the F1 score is improved from 74.7％to 77.1％compared with UFLDv2.
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