Computer Science ›› 2019, Vol. 46 ›› Issue (8): 292-297.doi: 10.11896/j.issn.1002-137X.2019.08.048

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

Track Defect Image Classification Based on Improved Ant Colony Algorithm

CAO Yi-qin, WU Dan, HUANG Xiao-sheng   

  1. (School of Software,East China Jiaotong University,Nanchang 330013,China)
  • Received:2018-06-30 Online:2019-08-15 Published:2019-08-15

Abstract: In view of the disadvantages of the traditional methods,such as low accuracy,slow classification speed,and a great difference in the recognition accuracy of different types of track defects,a new method of track defect image classification based on improved ant colony algorithm was proposed.The track defect image is preprocessed,the vertical projection method is used to extract the track surface area,the fuzzy theory and the hyper entropy theory are combined to obtain the best segmentation threshold,and the image segmentation is completed.Combined with adaptive threshold Canny edge detection operator and Hough transformation method,the rail defect part is determined.The edge details of defects are improved to make the contour of track defects more obvious.On the basis of this,the basic ant colony algorithm is analyzed,the characteristic similarity is used as a discriminant function,and the improved ant colony algorithm is used to classify the track defect image.Experimental results show that the classification accuracy and classification speed of the proposed method are high.

Key words: Classification, Defect image, Improved ant colony algorithm, Orbit

CLC Number: 

  • TP391
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