基于脉冲神经网络的铁路接触线异物检测研究  

Research on Foreign Object Detection in Railway Contact Line Based on Spiking Neural Network

作　　者：张永强[1,3] 刘健章 李向南 ZHANG Yongqiang;LIU Jianzhang;LI Xiangnan(School of Information Science and Engineering,Hebei University of Science and Technology,Shijiazhuang 050018,China;Shijiazhuang Chunxiao Internet Inf ormation Technology Co.,Ltd.,Shijiazhuang 050061,China;Hebei Technology Innovation Center of Intelligent IoT,Shijiazhuang 050018,China)

机构地区：[1]河北科技大学信息科学与工程学院,河北石家庄050018 [2]石家庄春晓互联网信息技术有限公司,河北石家庄050061 [3]河北省智能物联网技术创新中心,河北石家庄050018

出　　处：《软件工程》2025年第3期51-56,共6页Software Engineering

基　　金：河北省自然科学基金(F2022208002)。

摘　　要：为避免铁路接触线异物影响火车的正常行驶,文章提出一种基于脉冲神经网络的模型对接触线异物进行检测。首先,基于正常和异常的接触线图像编码得到的深度特征之间存在差距,实现对接触线异物的有效检测;其次,通过倒残差结构搭建脉冲序列生成模块;最后,基于脉冲神经网络的编码器提取特征信息。实验结果表明,在接触线异物检测数据集上,该模型的准确率和F 1分数分别为99.70%和99.70%。同时,在CIFAR-10(Canadian Institute for Advanced Research-10)和CIFAR-100(Canadian Institute for Advanced Research-100)数据集上的对比实验中,模型的准确率分别达到91.16%和79.54%。综上所述,该模型具有较强的分类检测能力,能够更准确地检测出异常接触线图像。To prevent foreign objects on railway contact lines from affecting the normal operation of trains,this paper proposes a model based on the Spiking Neural Network for detecting foreign objects on contact lines.Firstly,the disparity between deep features encoded from normal and abnormal contact line images is utilized to achieve effective detection of foreign objects.Secondly,an inverted residual structure is employed to construct a spike sequence generation module.Finally,a feature extraction encoder based on the Spiking Neural Network is used to extract feature information.Experimental results demonstrate that on the contact line foreign object detection dataset,the proposed model achieves an accuracy and F 1 score of 99.70%and 99.70%,respectively.Additionally,in comparative experiments on the CIFAR-10(Canadian Institute for Advanced Research-10)and CIFAR-100(Canadian Institute for Advanced Research-100)datasets,the accuracy of the model reaches 91.16%and 79.54%,respectively.In conclusion,the proposed model exhibits strong classification and detection capabilities,enabling more accurate identification of abnormal contact line images.

关 键 词：异物检测 铁路接触线 脉冲神经网络 倒残差结构 自注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]