ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2014, Vol. 51 ›› Issue (9): 1929-1935.doi: 10.7544/issn1000-1239.2014.20140153

所属专题: 2014深度学习

• 人工智能 • 上一篇    下一篇



  1. 1(轨道交通控制与安全国家重点实验室(北京交通大学) 北京 100044);2(北京交通大学交通数据分析与挖掘北京市重点实验室 北京 100044);3(深圳大学计算机与软件学院 广东深圳 518060);4(中国科学院计算技术研究所智能信息处理重点实验室 北京 100190) (
  • 出版日期: 2014-09-01
  • 基金资助: 

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

摘要: 基于缺陷检测应用中图像的稀疏特性,提出了缺陷图像的稀疏表示模型以及基于稀疏性的缺陷分解算法.在该模型中,缺陷图像表示为图像背景、缺陷目标和噪声3种成分的叠加,并且图像背景和缺陷目标可以分别由对应的冗余字典稀疏表示;然后借鉴盲源分离原理和块协调松弛方法,实现缺陷目标成分的有效分解;最后,在钢轨表面擦伤检测应用中验证了该算法的性能.

关键词: 稀疏表示, 缺陷检测, 盲源分离, 形态成分分析, 轨道检测

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