基于语义分割的实时车道线检测方法

Real-time lane detection method based on semantic segmentation

车道线识别是自动驾驶环境感知的一项重要任务。近年来,基于卷积神经网络的深度学习方法在目标检测和场景分割中取得了很好的效果。本文借鉴语义分割的思想,设计了一个基于编码解码结构的轻量级车道线分割网络。针对卷积神经网络计算量大的问题,引入深度可分离卷积来替代普通卷积以减少卷积运算量。此外,提出了一种更高效的卷积结构LaneConv和LaneDeconv来进一步提高计算效率。为了获取更好的车道线特征表示能力,在编码阶段本文引入了一种将空间注意力和通道注意力串联的双注意力机制模块(CBAM)来提高车道线分割精度。在Tusimple车道线数据集上进行了大量实验,结果表明,本文方法能够显著提升车道线的分割速度,且在各种条件下都具有良好的分割效果和鲁棒性。与现有的车道线分割模型相比,本文方法在分割精度方面相似甚至更优,而在速度方面则有明显提升。

Lane line recognition is an important task of automatic driving environment perception.In recent years,the deep learning method based on convolutional neural network has achieved good results in target detection and scene segmentation.Based on the idea of semantic segmentation,this paper designs a lightweight Lane segmentation network based on encoding and decoding structure.Aiming at the problem of large amount of computation of convolution neural network,the deep separable convolution is introduced to replace the ordinary convolution to reduce the amount of convolution computation.Moreover,a more efficient convolution structure of laneconv and lanedeconv is proposed to further improve the computational efficiency.Secondly,in order to obtain better lane line feature representation ability,in the coding stage,a dual attention mechanism module(CBAM)connecting spatial attention and channel attention in series is introduced to improve the accuracy of lane line segmentation.A large number of experiments are carried out on tusimple lane line data set.The results show that this method can significantly improve the lane line segmentation speed,and has a good segmentation effect and robustness under various conditions.Compared with the existing lane line segmentation models,the proposed method is similar or even better in segmentation accuracy,but significantly improved in speed.