Identification method of snow depth along high-speed railway based on deep convolutional neural network
BAO Yun
LI Junbo
CHEN Zhonglei
WEN Guiyu
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.
Keywords：high-speed railwaydeep convolutional neural networkimage segmentationsnow depth identification
Publication Date：2023-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 40-47 )