ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2014, Vol. 51 ›› Issue (9): 1929-1935.doi: 10.7544/issn1000-1239.2014.20140153

Special Issue: 2014深度学习

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Sparseness Representation Model for Defect Detection and Its Application

Li Qingyong1,2,4, Liang Zhengping3, Huang Yaping2, Shi Zhongzhi4   

  1. 1(State Key Laboratory of Rail Traffic Control and Safety (Beijing Jiaotong University), Beijing 100044);2(Beijing Key Laboratory of Transportation Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044);3(College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060);4(Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190)
  • Online:2014-09-01

Abstract: Defect detection is an important applicaion of computer vision in industry, but it is a challenge to effectively inspect defects in a vision system because of illumination inequality and the variation of reflection property of objects. Sparseness is one of the most improtant characteristics of defect images, and therefore the approach named defect decomposition based on sparseness (DDBS) is proposed. In the framework of DDBS, a defect image is treated as linearly combination of three components: background, defects and noise. Background is coded by an over-complete sparse dictionary, which can not sparsely represent defect component. Meanwhile defect is sparsely coded by its dictionary that is exclusive to background. Then defect component is decomposed using DDBS based on the principle of blind sources sepration and block-coordinate relaxation. Furthermore, DDBS is applied in rail surface defect detection to improve the robustness of inspection systems. Experiments are carried out for different dictionary combinations based on actual rail images, and the results demonstrate that DDBS can effectively detect the defects that are missed by the state-of-the-art methods. DDBS is a flexible framwork for applications of defect detection and has the potential benefit to improve robustness of traditional methods.

Key words: sparseness representation, defect detection, blind source sepration, morphological component analysis, rail detection

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