| 基于改进RepVGG网络的车道线检测算法 |
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| 引用本文： | 杨鹏强,张艳伟,胡钊政.基于改进RepVGG网络的车道线检测算法[J].交通信息与安全,2022,40(2):73-81. |
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| 作者姓名： | 杨鹏强 张艳伟 胡钊政 |
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| 作者单位： | 1.武汉理工大学交通与物流工程学院 武汉 430063 |
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| 摘 要： | 为提高自动驾驶系统中车道线检测的速度和精度,提出了基于可解耦训练状态与推理状态的车道线检测算法.在结构重参数化VGG(RepVGG)主干网络中引入注意力机制压缩-激励(SE)模块,增强对重要车道线信息的特征提取;同时设计并行可分离的辅助分割分支,对局部特征进行建模以提高检测精度.采用行方向位置分类车道线检测方式,在主干...
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| 关 键 词： | 自动驾驶 计算机视觉 车道线检测 RepVGG算法 偏移补偿 |
| 收稿时间： | 2021-11-10 |
A Lane Detection Algorithm Based on Improved RepVGG Network |
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| YANG Pengqiang,ZHANG Yanwei,HU Zhaozheng.A Lane Detection Algorithm Based on Improved RepVGG Network[J].Journal of Transport Information and Safety,2022,40(2):73-81. |
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| Authors: | YANG Pengqiang ZHANG Yanwei HU Zhaozheng |
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| Affiliation: | 1.School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China2.Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China |
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| Abstract: | To improve the speed and accuracy of lane detection of autonomous driving systems, a lane detection algorithm based on decoupled training and inference state is proposed. The attention mechanism Squeeze-and-Excitation(SE)module is employed in a Structural Re-parameterization VGG(RepVGG)backbone network to enhance the feature extraction of important information from lane lines' imageries. A separated parallelauxiliary segmentation branch is designed to model the local features for improving accuracy of detection. By adopting lane classification detection method in row direction, a row-by-row detection branch is added behind the backbone networkfor reducing calculation burden and realizing detection of shaded or defective lane lines. For restoring details of lane, an offset compensation branch is designed to horizontally refine the predicted position coordinates in partial range. The trained state model is decoupled by the structural re-parameterization method, and the multi-branch model is equivalently converted into a single-channel model to improve the speed and accuracy. Compared with un-decoupled model, the decoupled model's speed increases by 81%, and model size reduces by 11%. The proposed model is tested on the public lane detection data set CULane. It is compared with UFAST18 algorithm, which is the fastest in current lane detection model based on deep residual neural network. The result shows that the inference speed increases by 19%, the model size reduces by 12%, and the F1 -measure increases from 68.4 to 70.2. Its inference speed is 4 times that of the Self Attention Distillation(SAD) algorithm and 40 times that of the Spatial Convolutional Neural Networks(SCNN)algorithm. The actual vehicle experiment test is carried out in an urban area, and theresults of lane detectionare accurate and stable in various complex scenes such as congestion, curves, and shadows. The missingrate of lane detectionin common scenarios is between 10% and 20%. The test results show that the structural re-parameterization method is helpful for the optimization of the model, and the proposed lane detection algorithm can effectively improve the real-time and accuracy of the lane detection of the automatic driving system. |
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