Improved Sobel algorithm for defect detection of rail surfaces with enhanced efficiency and accuracy  

作　　者：石甜 孔建益 王兴东 刘钊 郑国 

机构地区：[1]School of Machinery and Automation,Wuhan University of Science and Technology

出　　处：《Journal of Central South University》2016年第11期2867-2875,共9页中南大学学报（英文版）

基　　金：Project(51174151)supported by the National Natural Science Foundation of China;Project(2010Z19003)supported by the Major Scientific Research Program of Hubei Provincial Department of Education,China;Project(2010CDB03403)supported by the Natural Science Foundation of Science and Technology Department of Hubei Province,China

摘　　要：A more effective and accurate improved Sobel algorithm has been developed to detect surface defects on heavy rails. The proposed method can make up for the mere sensitivity to X and Y directions of the Sobel algorithm by adding six templates at different directions. Meanwhile, an experimental platform for detecting surface defects consisting of the bed-jig, image-forming system with CCD cameras and light sources, parallel computer system and cable system has been constructed. The detection results of the backfin defects show that the improved Sobel algorithm can achieve an accurate and efficient positioning with decreasing interference noises to the defect edge. It can also extract more precise features and characteristic parameters of the backfin defect. Furthermore, the BP neural network adopted for defects classification with the inputting characteristic parameters of improved Sobel algorithm can obtain the optimal training precision of 0.0095827 with 106 iterative steps and time of 3 s less than Sobel algorithm with 146 steps and 5 s. Finally, an enhanced identification rate of 10% for the defects is also confirmed after the Sobel algorithm is improved.A more effective and accurate improved Sobel algorithm has been developed to detect surface defects on heavy rails. The proposed method can make up for the mere sensitivity to X and Y directions of the Sobel algorithm by adding six templates at different directions. Meanwhile, an experimental platform for detecting surface defects consisting of the bed-jig, image-forming system with CCD cameras and light sources, parallel computer system and cable system has been constructed. The detection results of the backfin defects show that the improved Sobel algorithm can achieve an accurate and efficient positioning with decreasing interference noises to the defect edge. It can also extract more precise features and characteristic parameters of the backfin defect. Furthermore, the BP neural network adopted for defects classification with the inputting characteristic parameters of improved Sobel algorithm can obtain the optimal training precision of 0.0095827 with 106 iterative steps and time of 3 s less than Sobel algorithm with 146 steps and 5 s. Finally, an enhanced identification rate of 10% for the defects is also confirmed after the Sobel algorithm is improved.

关 键 词：Sobel algorithm surface defect heavy rail experimental platform IDENTIFICATION 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]