基于注意力机制与动态蛇形卷积的车道线分割算法  

Lane segmentation algorithm based on attention mechanism and dynamic snake convolution

作　　者：宋百玲 李星禹 刘伟 邓俊熙 牟俊麒 SONG Bailing;LI Xingyu;LIU Wei;DENG Junxi;MU Junqi(College of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin 150006,China)

机构地区：[1]东北林业大学机电工程学院,黑龙江哈尔滨150006

出　　处：《山东科学》2025年第1期129-140,共12页Shandong Science

基　　金：黑龙江省“百千万工程”科技重大专项(2021ZX04A01)。

摘　　要：车道检测作为计算机视觉技术在交通领域的关键应用,具有深远的实用价值。尽管如此,现有的语义分割网络模型在道路语义分割任务中仍面临着精度不足和边缘模糊等挑战。针对这些问题,提出了一种基于UNet模型的改进型车道线图像分割网络架构。在UNet模型的跳跃连接中,引入双重注意力机制,优先突出了车道线的重要性,并有效降低了噪声的干扰。此外,采用了动态蛇形卷积来替代传统的卷积方法,增强了网络对车道线的识别能力。考虑到在曝光不足或光线较暗的背景下进行车道线检测的挑战,在图像预处理阶段引入了一种改进的自适应Gamma校正技术,以增强检测的全面性和准确性。同时,在编码器末端引入了空洞金字塔池化技术。实验结果表明,在满足实时性要求的前提下,该模型TuSimple数据集上达到了98.93%的准确率,相较于其他5种基于语义分割的车道线检测算法,展现出更优越的识别效果,结果验证了应用动态蛇形卷积与双注意力机制改进的有效性。Lane detection is a remarkable practical application of computer vision technology in the field of transportation.However,existing semantic segmentation network models still face certain challenges such as insufficient accuracy and blurred edges in road semantic segmentation tasks.To address these issues,an improved lane segmentation network architecture based on the UNet model is proposed.First,a dual attention module(DAM)is introduced in the skip connections of the UNet model,which prioritizes the importance of lane lines and effectively reduces noise interference.Additionally,dynamic snake convolution(DSConv)is employed to replace traditional convolution methods,enhancing the network’s lane detection ability.To enhance the comprehensiveness and accuracy of lane detection in underexposed or dark backgrounds,an improved adaptive Gamma correction method is introduced in the image preprocessing stage.Furthermore,atrous spatial pyramid pooling(ASPP)technology is introduced at the end of the encoder to enhance network performance.Experimental results show that this model achieves an accuracy of 98.93%on the TuSimple dataset while meeting real-time requirements.Compared to five other semantic segmentation-based lane detection algorithms,the proposed algorithm demonstrates superior recognition performance,thus validating its effectiveness.

关 键 词：车道线检测 语义分割 注意力机制 动态蛇形卷积 Gamma校正算法 

分 类 号：U491.6[交通运输工程—交通运输规划与管理]