北京交通大学学报2023，Vol.47Issue(5)：40-47,8.DOI:10.11860/j.issn.1673-0291.20220113
基于深度卷积神经网络的高速铁路积雪深度判识方法
Identification method of snow depth along high-speed railway based on deep convolutional neural network
摘要
Abstract
To address the issue of dynamic snow depth recognition on high-speed railway tracks,this paper proposes a snow depth identification method based on comprehensive railway video image recog-nition.Firstly,the snow depth images obtained from the comprehensive video monitoring system are processed.The U-Net neural network is used to segment the images,thereby establishing a dataset of snow depths on the tracks.Subsequently,the snow depth dataset is annotated by categorizing the snow depth images into three classes:below 100 mm,100 mm to the rail surface,and above the rail surface.Based on this dataset,a snow depth image recognition method is established using the DenseNet-201 deep convolutional neural network model.Finally,the model is validated.The re-search results indicate that for images with good lighting conditions,the recognition accuracy of the DenseNet-201 deep convolutional neural network model reaches 93.57%.Compared to the recogni-tion results of other models like VGG-16 and ResNet-50,although the DenseNet-201 deep convolu-tional neural network model has a longer computation time than the ResNet-50 model,it improves rec-ognition accuracy by 2.08%and 4.24%compared to ResNet-50 and VGG-16 models,respectively.The research results can provide technical support for the dynamic identification of snow depth along high-speed railways.关键词
高速铁路/深度卷积神经网络/图像分割/积雪深度判识Key words
high-speed railway/deep convolutional neural network/image segmentation/snow depth identification分类
信息技术与安全科学引用本文复制引用
包云,李俊波,陈中雷,温桂玉..基于深度卷积神经网络的高速铁路积雪深度判识方法[J].北京交通大学学报,2023,47(5):40-47,8.基金项目
国家重点研发计划(2022YFB4300604) （2022YFB4300604）
中国铁道科学研究院集团有限公司科研项目(2021YJ303) （2021YJ303）
北京经纬信息技术有限公司博士基金(DZYF22-10) National Key R&D Plan(2022YFB4300604) （DZYF22-10）
Project Research Project of China Academy of Railway Sciences Group Co.,Ltd.(2021YJ303) （2021YJ303）
Doctoral Fund of Beijing Jingwei Information Technology Co.,Ltd.(DZYF22-10) （DZYF22-10）
