Automatic Thresholding for Defect Detection

Automatic thresholding has been widely used in the machine vision industry for automated visual inspection of defects. A commonly used thresholding technique, the Otsu method, provides satisfactory results for thresholding an image with histogram of bimodal distribution. This method, however, fails if the histogram is unimodal or close to unimodal. For defect detection applications, defects range from no defect, small defect, to large defect, which means the gray-level distributions range from unimodal to bimodal. In this paper, we revised and improved the Otsu method for selecting optimal threshold values for both unimodal and bimodal distributions. We also tested the performance of the revised method on common defect detection applications.