基于注意力机制与线锚信息传递的车道线检测  

Lane Line Detection Based on Attention Mechanism and Line Anchor Information Transmission

作　　者：姜俊昭[1,2] 彭彬 杨文豪 徐业凯 JIANG Junzhao;PENG Bin;YANG Wenhao;XU Yekai(Hefei University of Technology,Hefei 230041,China;Key Laboratory for Automated Vehicle Safety Technology of Anhui Province,Hefei 230041,China)

机构地区：[1]合肥工业大学,合肥230041 [2]安徽省自动驾驶汽车安全技术重点实验室,合肥230041

出　　处：《汽车工程学报》2024年第5期812-820,共9页Chinese Journal of Automotive Engineering

基　　金：安徽省新能源汽车暨智能网联汽车创新工程项目(JZ2021AFKJ0002);中央高校基本科研业务费专项资金资助项目(JZ2023YQTD0073);安徽省重点实验室自主创新专项(PA2023GDSK0113):面向高级别自动驾驶的车道线多分类检测算法研究。

摘　　要：车道线检测是自动驾驶领域的关键技术,目前仍面临较多挑战。车道线监督信号的稀疏性以及复杂场景下的遮挡、阴影等因素会影响检测的准确率与实时性。基于此,提出了一种融合CBAM注意力机制与线锚特征聚合模块的车道线检测模型,提出的算法在Tusimple和CULane数据集分别达到96.19%的准确率和76.24%的综合F1得分,通过实车测试表明,该算法检测帧率为67 fps,可以在复杂交通场景下进行实时检测,较好地解决了车道线遮挡问题。Lane line detection is a key technology in the field of autonomous driving,and it currently faces many challenges.The sparsity of the lane line supervision signal,as well as factors such as occlusion and shadows in complex scenes,can affect detection accuracy and real-time performance.Based on this,this paper proposes a lane line detection model that integrates the CBAM attention mechanism and a line anchor feature aggregation module.The proposed algorithm achieves an accuracy of 96.19%and a comprehensive F1 score of 76.24%on the Tusimple and CULane datasets,respectively.Real vehicle tests show that the algorithm detects a frame rate of 67 fps,allowing for real-time detection in complex traffic scenarios and more effectively addressing the problem of lane line occlusion.

关 键 词：车道线检测 线锚 注意力机制 信息传递 

分 类 号：U471.15[机械工程—车辆工程]