Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 247-249.doi: 10.11896/JsJkx.191000049

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Surface Defect Detection Method of Industrial Products Based on Histogram Difference

YANG Zhi-wei1, DAI Ming2 and ZHOU Zhi-heng2   

  1. 1 China Academy of Information and Communications Technology (Guangzhou Telecommunication Zhixin Technology Co.,Ltd.),Guangzhou
    510060,China2 School of Electronics and Information Engineering,South China University of Technology,Guangzhou 510640,China
  • Published:2020-07-07
  • About author:YANG Zhi-wei, born in 1968, bachelor, senior engineer.His main research interests include industrial internet, 5G applications and planning, consulting, design of smart cities, also with the establishment of industrialization and information integration system.

Abstract: With the rapid development of computer vision,human labor is gradually replaced by machine vision in product detection,especially in the production environment that workers should not stay long.Automatic detection of surface defects of industrial products is an inevitable trend of modern industry.In this paper,defect detection is regarded as a special image segmentation problem,and it is extracted by taking product surface as the background and surface defects as the foreground.In this paper,the segmentation is based on the difference between the gray distribution histogram of the foreground and the background,and the similarity between the background and prior background distribution histogram.Combining nonparametric statistical activity contour model and prior distribution,the gray distribution of the product surface is considered as the background prior information to construct the corresponding energy function,then the corresponding iteration equation of level set function is obtained by minimizing the energy function,so the defect detection can be more efficient.Experiments show that the proposed defect detection method is improved significantly in vision and numerical indexes such as detection accuracy,false alarm and missing detection.

Key words: Active contour model, Defects detection, Image segmentation, Level set, Prior distribution

CLC Number: 

  • TP391
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