Development of a new software for fabric defect detection and classification using image processing and machine learning methods

Quality control in the fabric industry involves a set of standards or guidelines that help guarantee a product meets certain parameters as well as customer satisfaction. Fabric defect detection (also called inspection) is a quality control process aimed at identifying and locating defects. The aim of this thesis is to build an application based on image processing and deep learning methods to automatically detect the defects on the fabric surface and classify them. Discrete Fourier transform (DFT). Normalized cross-correlations, homogeneity equalization, and Gabor filters have been used in image processing, Faster Region Proposal Networks (Faster-R CNN) has been used in classification. The mean square errors (RMSE's) has been used for computing the detecting and classification errors. Based on the results obtained, it has been proved that image processing methods especially DFT can be used for the defect detection with acceptable results.