计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (7): 236-243.doi: 10.11896/jsjkx.230400128

• 计算机图形学&多媒体 • 上一篇    下一篇

基于RepVGG网络的实时车道线检测方法

蔡汶良, 黄俊   

  1. 重庆邮电大学通信与信息工程学院 重庆 400065
  • 收稿日期:2023-04-18 修回日期:2023-09-20 出版日期:2024-07-15 发布日期:2024-07-10
  • 通讯作者: 黄俊(huangjun@cqupt.edu.cn)
  • 作者简介:(cwl990110@163.com)
  • 基金资助:
    国家自然科学基金(61771085)

Lane Detection Method Based on RepVGG

CAI Wenliang, HUANG Jun   

  1. School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Received:2023-04-18 Revised:2023-09-20 Online:2024-07-15 Published:2024-07-10
  • About author:CAI Wenliang,born in 1999,postgra-duate.His main research interests include object detection and deep lear-ning.
    HUANG Jun,born in 1971,Ph.D,professor,master supervisor.His main research interests include object detection and deep learning.
  • Supported by:
    National Natural Science Foundation of China(61771085).

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摘要： 针对现有车道线检测方法存在的检测速度慢、检测精度低的问题,将车道线检测视为分类问题,提出了基于RepVGG网络的实时车道线检测方法。在RepVGG网络中融合不同层级特征图,减少空间定位信息的损失,提高车道线的定位精度。采用曲线建模的后处理方法,从整体和局部两个角度修正车道线预测结果。挖掘车道线定位中的分布信息,提出了基于分布指导的车道线存在预测分支,直接从车道线定位分布中学习车道线的存在特征,在略微提升推理速度的同时进一步提升检测精度。在TuSimple和CULane数据集上的实验表明,该模型在检测速度和精度上取得了良好的平衡。在CULane数据集上,所提方法的推理速度为目前同类方法中检测速度最快的UFLDv2算法的1.13倍,同时F1分数从74.7％提高到77.1％,达到了实时检测任务的需求。

关键词: 计算机视觉, RepVGG, 车道线检测, 曲线拟合, 特征融合, 后处理

Abstract: Aiming at the problems of slow detection speed and low detection accuracy in existing lane detection methods,lane detection 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 dataset 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.

Key words: Computer vision, RepVGG, Lane detection, Curve fitting, Feature fusion, Post-processing

中图分类号: 

  • TP391
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