Leaky Cable Fixture Detection in Railway Tunnel Based on RW DCGAN and Compressed GS-YOLOv5  

作　　者：Suhang Li Yunzuo Zhang Ruixue Liu Jiayu Zhang Zhouchen Song Yutai Wang 

机构地区：[1]School of Information Science and Technology,Shijiazhuang Tiedao University,Shijiazhuang,050043,China [2]Australian National University,Canberra,2600,ACT,Australia

出　　处：《Intelligent Automation & Soft Computing》2023年第7期1163-1180,共18页智能自动化与软计算（英文）

基　　金：supported by the National Natural Science Foundation of China(No.61702347,No.62027801);Natural Science Foundation of Hebei Province(No.F2022210007,No.F2017210161);Science and Technology Project of Hebei Education Department(No.ZD2022100,No.QN2017132);Central Guidance on Local Science and Technology Development Fund(No.226Z0501G);National innovation and Entrepreneurship training program for college students(No.202110107024).

摘　　要：The communication system of high-speed trains in railway tunnels needs to be built with leaky cables fixed on the tunnel wall with special fixtures.To ensure safety,checking the regular leaky cable fixture is necessary to elimi-nate the potential danger.At present,the existing fixture detection algorithms are difficult to take into account detection accuracy and speed at the same time.The faulty fixture is also insufficient and difficult to obtain,seriously affecting the model detection effect.To solve these problems,an innovative detection method is proposed in this paper.Firstly,we presented the Res-Net and Wasserstein-Deep Convolution GAN(RW-DCGAN)to implement data augmentation,which can enable the faulty fixture to export more high-quality and irregular images.Secondly,we proposed the Ghost SENet-YOLOv5(GS-YOLOv5)to enhance the expression of fixture feature,and further improve the detection accuracy and speed.Finally,we adopted the model compression strategy to prune redundant channels,and visualized training details with Grad-CAM to verify the reliability of our model.Experimental results show that the algorithm model is 69.06%smaller than the original YOLOv5 model,with 70.07%fewer parameters,2.1%higher accuracy and 14.82 fps faster speed,meeting the needs of tunnel fixture detection.

关 键 词：Leaky cable fixture detection RW-DCGAN compressed strategy GS-YOLOv5 

分 类 号：U45[建筑科学—桥梁与隧道工程]