Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 274-277.

• Pattem Recognition & Image Processing • Previous Articles     Next Articles

Research on Intelligent Detection Method of Steel Rail Abrasion

ZHANG Xiu-feng, WANG Juan, DING Qiang   

  1. College of Electromechanical Engineering,Dalian Nationalities University,Dalian,Liaoning 116600,China
  • Online:2019-02-26 Published:2019-02-26

Abstract: In order to meet the actual demand,a new detection method of steel rail abrasion based on line laser image processing was proposed after analyzing current methods and characteristics of steel rail abrasion detecting equipment at home and abroad.The bending degree of line laser image on the wear of rail was used to determine the width and depth of steel rail abrasion.The edge points and centre points could be found by using roof-type edge detection method,then straight lines can be fitted by using these points.The optimal features combination is selected by removing the redundant features with high correlation.Finally,the experiment results show that the method could extract features amount effectively,and obtain the width and depth of the steel rail abrasion accurately.The characteristics of algorithm inculde small amount,simple and high precision.It lays the foundation for the development of steel rail abrasion detection device.

Key words: Correlation coefficient, Edge detection, Image processing, Steel rail abrasion

CLC Number: 

  • TH74
[3]JIN W R,ZHAN X Q,JIANG B H.Non-contact Rail-Wear Inspecting System Based on Image Understanding [C]∥Procee-ding of the 2007 IEEE International Conference on Mechatronics And Automation.Harbin,2007:3854-3858.
[11]BOGDAN M,FITA S.Measurement of the Geometry of the Transverse Cross-section of a railway[J].Measurement Science Review,2003(3):747-751.
[13]JIN W R,ZHANG X Q,JIANG B H.Non-Contact Rail-wear Inspecting System Based on Image Understanding[C]∥Procee-ding of the 2007 IEEE,International Conference on Mechatronics and Automation.Harbin,2007:3854-3858.
[1] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[2] 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.
[3] 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.
[4] LUO Jing-jing, TANG Wei-zhen, DING Ji-ting. Research of ATC Simulator Training Values Independence Based on Pearson Correlation Coefficient and Study of Data Visualization Based on Factor Analysis [J]. Computer Science, 2021, 48(6A): 623-628.
[5] SONG Yu, SUN Wen-yun. Edge Detection in Images Corrupted with Noise Based on Improved Nonlinear Structure Tensor [J]. Computer Science, 2021, 48(6): 138-144.
[6] WANG Yi-hao, DING Hong-wei, LI Bo, BAO Li-yong, ZHANG Ying-jie. Prediction of Protein Subcellular Localization Based on Clustering and Feature Fusion [J]. Computer Science, 2021, 48(3): 206-213.
[7] 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.
[8] 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.
[9] YAO Nan, ZHANG Zheng. Scar Area Calculation Based on 3D Image [J]. Computer Science, 2021, 48(11A): 308-313.
[10] ZHU Rong, YE Kuan, YANG Bo, XIE Huan, ZHAO Lei. Feature Classification Method Based on Improved DeeplabV3+ [J]. Computer Science, 2021, 48(11A): 382-385.
[11] 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.
[12] 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.
[13] 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.
[14] LIU Jun-qi, LI Zhi and ZHANG Xue-yang. Candidate Region Detection Method for Maritime Ship Based on Visual Saliency [J]. Computer Science, 2020, 47(6A): 237-241.
[15] SONG Ya-fei, CHEN Yu-zhang, SHEN Jun-feng and ZENG Zhang-fan. Underwater Image Reconstruction Based on Improved Residual Network [J]. Computer Science, 2020, 47(6A): 500-504.
Full text



No Suggested Reading articles found!