Thresholding Based on Maximum Weighted Object Correlation for Rail Defect Detection

Qingyong LI  Yaping HUANG  Zhengping LIANG  Siwei LUO  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E95-D   No.7   pp.1819-1822
Publication Date: 2012/07/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E95.D.1819
Print ISSN: 0916-8532
Type of Manuscript: Special Section LETTER (Special Section on Machine Vision and its Applications)
Category: Image Processing
Keyword: 
automatic thresholding,  defect detection,  correlation,  

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Summary: 
Automatic thresholding is an important technique for rail defect detection, but traditional methods are not competent enough to fit the characteristics of this application. This paper proposes the Maximum Weighted Object Correlation (MWOC) thresholding method, fitting the features that rail images are unimodal and defect proportion is small. MWOC selects a threshold by optimizing the product of object correlation and the weight term that expresses the proportion of thresholded defects. Our experimental results demonstrate that MWOC achieves misclassification error of 0.85%, and outperforms the other well-established thresholding methods, including Otsu, maximum correlation thresholding, maximum entropy thresholding and valley-emphasis method, for the application of rail defect detection.