A Defect Detection Method for the Image With Intersecting Feature

Abstract

Since the intersecting feature between the defect and the background of the image, the defect detection often results in under-segmentation or over-segmentation. To solve this problem, we propose a new defect extraction method by calculating the maximum mutual information of intersecting features. Firstly, we construct a new two-dimensional histogram according to the defect features. The new histogram is called Gray Level and Local Spatial Difference histogram (GLSD), which is constructed by grayscale and the improved local gray difference with the spatial relationship. Secondly, considering the geometric distribution of high-probability background events, we improve the segmentation shape of the background event distribution and divide the GLSD histogram preliminary. Finally, we calculate the maximum mutual information of the intersecting feature between the defect and the background. At this point, the boundary of the intersecting feature interval of the GLSD histogram is determined. To verify the effectiveness of the proposed method, we used two sets of databases for performance evaluation. The experimental results show that the proposed method is suitable for non-obvious defect detection under the local uniform background. Meanwhile, it can improve the sensitivity, specificity, and accuracy of defect detection compared with the classical threshold segmentation methods.