基于改进RepVGG网络的车道线检测算法

A Lane Detection Algorithm Based on Improved RepVGG Network

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,在主干网络后加入逐行检测分支,减小计算量的同时实现对遮挡或缺损车道线的检测;设计偏移补偿分支,在水平方向上细化局部范围内预测的车道线位置坐标,以恢复车道细节。通过结构重参数化方法解耦训练状态模型,将多分支模型等价转换为单路模型,以提高推理状态模型的速度和精度。对比解耦前后的模型,本研究算法速度提高81%,模型规模减小11%。利用车道线检测数据集CULane对算法进行测试,与目前基于深度残差神经网络的车道线检测模型中检测速度最快的UFAST18算法相比,其检测速度提高19%,模型规模减小12%,评价指标F1-measure由68.4增长到70.2;本研究算法的检测速度是自注意力蒸馏(SAD)算法的4倍,空间卷积神经网络(SCNN)算法的40倍。通过城区实车实验测试,在拥挤、弯道、阴影等多种复杂场景下车道线检测结果准确稳定,常见场景下车道线漏检率在10%~20%之间。测试结果表明,结构重参数化方法有助于模型优化,提出的车道线检测算法能有效提高自动驾驶系统的车道线检测实时性和准确性。

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 4times 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.