Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 230200101-6.doi: 10.11896/jsjkx.230200101

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Railway Track Detection Method Based on Improved YOLOv5s

JIANG Ke, SHI Jianqiang, CHEN Guangwu   

  1. 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
  • Published:2023-11-09
  • About author:JIANG Ke,born in 1997,postgraduate.Her main research interests include train tracking and safety.
  • Supported by:
    Gansu Science and Technology Plan Projects(21ZD4WA018,2020-61-14,22YF7GA140,18JR3RA131),Science and Technology Project of Education Department of Gansu Province(2017-A24) and Lanzhou Talent Innovation and Entrepreneurship Project(2022-RC-56).

PDF (PC)

262

Abstract: 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.

Key words: Train front environmental understanding, Railway track line, YOLOv5s, Image processing, CBAM module

CLC Number: 

  • U213
[1]XIAO Y,SU L Y.Research on the Integrated Technology of the Automatic Driving System for Rail Transit[J].China Railway,2015,5(5):109-113.
[2]LIU D W,GUO J,WANG X M,et al.Intelligent MonitoringTechnologies forRailway Signaling Systems in China[J].Journal of Southwest Jiaotong University,2014,49(9):904-912.
[3]APKING A.Worldwide Market for Railway Industries Study:Market Volumes for OEM Business and After-Sales Service as Well as Prospects for Market Developments of Infrastructure and Rolling Stock[J].SCI Verkehr GmbH:Köln,Germany,2018:7.
[4]XIAO J S,CHENG X,LI B J,et al.Lane detection algorithmbased on Beamlet transformation and K-means clustering[J].Journal of Sichuan University(Engineering Science Edition),2015,47(4):98-103.
[5]ETXEBERRIA-GARCIA M,LABAYEN M,ZAMALLOA M,et al.Application of computer vision and deep learning in the railway domain for autonomous train stop operation[C]//2020 IEEE/SICE International Symposium on System Integration(SII).IEEE,2020:943-948.
[6]KAPOOR R,GOEL R,SHARMA A.Deep learning based object and railway track recognition using train mounted thermal imaging system[J].Journal of Computational and Theoretical Nanoscience,2020,17(11):5062-5071.
[7]DENG Y W,PU H T,HUA X B.Research on Lane Detection Based On RC-DBSCAN[J].Journal of Hunan University(Natural Sciences),2021,48(10):85-92.
[8]MUTHALAGU R,BOLIMERA A,KALAIC V.Lane detection technique based on perspective transformation and histogram analysis for self-driving cars[J].Computers & Electrical Engineering,2020,85:106653.
[9]WEI X,YANG Z,LIU Y,et al.Railway track fastener defect detection based on image processsing and deep learning techniques:A comparative study[J].Engineering Applications of Artificial Intelligence,2019,80:66-81.
[10]HE H,LIU A,QIU J.Lane Line Detection Method Based on MFD-MCN[J].Journal of Hunan Institute of Engineering(Na-tural Science Edition),2022,32(2):48-53.
[11]QIN N,HU X,DAI H.Deep fusion of multi-view and multimodal representation of ALS point cloud for 3D terrain scene re-cognition[J].ISPRS Journal of Photogrametry and Remote Sensing,2018,143:205-212.
[12]WANG J T,HONG W,GONG L.Lane detection algorithmbased ondensity clustering and RANSAC[C]//2018 Chinese Control And Decision Conference(CCDC).Shenyang,China:IEEE,2018:919-924.
[13]LI X J,DENG Y M,CHEN Z H,et al.Improved YOLOv5’s Foreign Object Debris Detection Algorithm for Airport Runways[J].Computer Engineering and Applications,2023,59(2):202-211.
[14]WOO S,PARK J,LEE J Y,et al.Cbam:Convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:3-19.
[15]WEI X K,SUO D,WEI D H.A Survey of The Application of Machine Vision In Rail Transit System Inspection[J].Control and Decision,2021,36(2):257-282.
[16]WANG S,LIU F,LIU B.Research on application of deep convolutional network in high-speed railway track inspection based on distributed fiber acoustic sensing[J].Optics Communications,2021,492:126-141.
[17]WANG G,CHEN X C,HAN D Y.Research on threshold segmentation algorithm of coal CT images based on improved Otsu[J].Coal Science and Technology,2021,49(1):264-271.
[18]YUE X X,JIA J X,CHEN X D,et al.Road small target detection algorithm based on improved YOLO V3[J].Computer Engineering and Applications,2020,56(21):218-223.
[19]LU D,MA W Q.Gesture Recognition Based on ImprovedYOLOv4-tiny Algorithm[J].Journal of Electronics & Information Technology,2021:1-9.
[20]LYU Z,ZHANG D,LUO J.A GPU-free real-time object detection method for apron surveillance video based on quantized MobileNet-SSD[J].IET Image Processing,2022.
[1] YU Jiuyang, ZHANG Dean, DAI Yaonan, HU Tianhao, XIA Wenfeng. Image Super-resolution Reconstruction Based on Structured Fusion Attention Network [J]. Computer Science, 2023, 50(6A): 220600240-5.
[2] DAI Tianhong, SONG Jieqi. Multimodal MRI Brain Tumor Segmentation Based on Multi-encoder Architecture [J]. Computer Science, 2023, 50(6A): 220200108-6.
[3] JIANG Bo, WAN Yi, XIE Xianzhong. Improved YOLOv5s Lightweight Steel Surface Defect Detection Model [J]. Computer Science, 2023, 50(11A): 230900113-7.
[4] ZHANG Xinfeng, BIAN Haonan, ZHANG Bo, ZHANG Jiaming, LIANG Yuqing. Rail Light Band Detection Algorithm Based on Deep Learning [J]. Computer Science, 2023, 50(11A): 230200146-6.
[5] CHEN Meiying, BI Xiuli, LIU Bo. Image Retargeting Method Based on Grids and Superpixels [J]. Computer Science, 2023, 50(11A): 221100153-8.
[6] LIU Wei-ye, LU Hui-min, LI Yu-peng, MA Ning. Survey on Finger Vein Recognition Research [J]. Computer Science, 2022, 49(6A): 1-11.
[7] LAI Teng-fei, ZHOU Hai-yang, YU Fei-hong. Real-time Extend Depth of Field Algorithm for Video Processing [J]. Computer Science, 2022, 49(6A): 314-318.
[8] PAN Ze-min, QIN Ya-li, ZHENG Huan, WANG Rong-fang, REN Hong-liang. Block-based Compressed Sensing of Image Reconstruction Based on Deep Neural Network [J]. Computer Science, 2022, 49(11A): 210900118-9.
[9] HE Huang-xing, CHEN Ai-guo, WANG Jiao-long. Handwritten Image Binarization Based on Background Estimation and Local Adaptive Integration [J]. Computer Science, 2022, 49(11): 163-169.
[10] ZHAN Rui, LEI Yin-jie, CHEN Xun-min, YE Shu-han. Street Scene Change Detection Based on Multiple Difference Features Network [J]. Computer Science, 2021, 48(2): 142-147.
[11] ZHANG Yu-long, WANG Qiang, CHEN Ming-kang, SUN Jing-tao. Survey of Intelligent Rain Removal Algorithms for Cloud-IoT Systems [J]. Computer Science, 2021, 48(12): 231-242.
[12] XIE Hai-ping, LI Gao-yuan, YANG Hai-tao, ZHAO Hong-li. Classification Research of Remote Sensing Image Based on Super Resolution Reconstruction [J]. Computer Science, 2021, 48(11A): 424-428.
[13] YAO Nan, ZHANG Zheng. Scar Area Calculation Based on 3D Image [J]. Computer Science, 2021, 48(11A): 308-313.
[14] FENG Yi-fan, ZHAO Xue-qing, SHI Xin, YANG Kun. Light Superposition-based Color Constancy Computational Method [J]. Computer Science, 2021, 48(11A): 386-390.
[15] SONG Yi-yan, TANG Dong-lin, WU Xu-long, ZHOU Li, QIN Bei-xuan. Study on Digital Tube Image Reading Combining Improved Threading Method with HOG+SVM Method [J]. Computer Science, 2021, 48(11A): 396-399.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.