Automated defect inspection and classification of leather fabric

Abstract

This paper describes an automated vision system for detecting and classifying surface defects on leather fabric. In the defect inspection process, visual defects are located and reported through a two-step segmentation procedure based on thresholding and morphological processing. In the defect classification process, the system utilizes both geometric and statistical features as its feature sets; that is, a new normalized compactness measure, and first- and second-order statistical features. In an effort to maximize the classification efficiency, a three-stage sequential decision-tree classifier is adopted for the classification of five types of defects: lines, holes, stains, wears, and knots. If line defects are identified as a result of classification, they are checked by a line combination algorithm to determine if they are parts of larger line defects and, in such a case, are reported as combined line defects. Satisfactory results were achieved in the classification test with an overall accuracy of 91.25%