Affiliations: [a] School of Industrial Engineering, Purdue University, West Lafayette, IN 47907-1287, USA | [b] Department of Industrial and Manufacturing Engineering, Pennsylvania State University, University Park, PA 16802, USA | [c] Westwood Industries, 597 Glasgow Lane, Tupeco, MS 38803, USA
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%
