基于改进的YOLOv5s列车轨道线检测方法  

Railway Track Detection Method Based on Improved YOLOv5s

作　　者：姜珂 石建强 陈光武 JIANG Ke;SHI Jianqiang;CHEN Guangwu(Key Laboratory of Opt-Electonic Technology and Intelligent Control Ministry of Education,Lanzhou 730070,China;School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou Jiaotong University,Lanzhou 730070,China;Gansu Provincial Key Laboratory of Traffic Information Engineering and Control,Lanzhou 730070,China)

机构地区：[1]兰州交通大学光电技术与智能控制教育部重点实验室,兰州730070 [2]兰州交通大学自动化与电气工程学院,兰州730070 [3]甘肃省高原交通信息工程及控制重点实验室,兰州730070

出　　处：《计算机科学》2023年第S02期295-300,共6页Computer Science

基　　金：甘肃省科技计划项目(21ZD4WA018,2020-61-14,22YF7GA140,18JR3RA131);甘肃省教育厅科技项目(2017-A24);兰州市人才创新创业项目(2022-RC-56)。

摘　　要：轨道线检测有助于提高列车的行驶安全,但检测效果易受列车行驶环境的影响。针对这种情况,提出了基于图像预处理并使用改进后的YOLOv5s网络进行轨道线检测的方法。首先,对图像预处理,使用HSV分离出图像的多余信息后,基于Otsu阈值处理,提高了图像检测目标的显著度,降低了目标识别的复杂程度;其次,考虑到列车车载系统轻量化的要求,对YOLOv5s目标识别网络进行了改进,通过添加CBAM注意力机制模块改进主干网络,来加强有效的特征信息,可以在确保检测结果的基础上提高检测速度,并使得检测算法模型易于部署到移动端设备中。使用公开的列车行驶图像构建数据集进行实验,实验结果表明提出的检测算法在数据集测试中的mAP达到了94.1%,具备一定的实时性和鲁棒性。Track line detection is helpful to improve the running safety of the train,but the detection effect is easily affected by the running environment of the train,this paper proposes a method based on image pre-processing and using the improved YOLOv5s network for track line detection.Firstly,the image pre-processing,using HSV to separate out the redundant information of the image and then based on Otsu thresholding,improves the saliency of the image detection target and reduces the complexity of target recognition.Secondly,considering the requirement of light weight of the train on-board system,the YOLOv5s target recognition network is improved,and the backbone network is improved by adding CBAM attention mechanism module to enhance the effective feature information,which can improve the detection speed on the basis of ensuring the detection results and make the detection algorithm model easy to deploy to mobile devices.Experimental results show that the proposed detection algorithm achieves 94.1%mAP in the dataset test with certain real-time performance and robustness.

关 键 词：列车前方环境理解 列车轨道线 YOLOv5s 图像处理 CBAM模块 

分 类 号：U213[交通运输工程—道路与铁道工程]