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AbstractIn this study, a machine vision system is developed to achieve fabric inspection and defect classification processes automatically. The system consists of an image acquisition hardware and an image processing software. A simple and portable system was designed so that it can be adapted easily to all types of the fabric inspection machines. The software of the system consists of defect detection and classification algorithms. The defect detection algorithm is based on wavelet transform, double thresholding binarization, and morphological operations. It was applied real time via a user interface prepared by using MATLAB® program. The defect classification approach is based on gray level co-occurrence matrix and feed forward neural network. Five commonly occurring defect types, warp lacking, weft lacking, soiled yarn hole, and yarn flow, were detected and classified. The defective and defect-free regions of the fabric were detected with an accuracy of 93.4% and the defects are classified with 96.3% accuracy rate.Keywords: fabric inspectiondefect classificationmachine vision systemwavelet transformneural network AcknowledgementsThis study is a project supported by Gaziantep University Scientific Research Projects Management Unit. The name of project is 'Development an Intelligent System for Fabric Defect Detection' and the project number is MF.10.12.
Çelık et al. (Mon,) studied this question.