A thermal-based defect classification method in textile fabrics with K-nearest neighbor algorithm

In this study, fabric defects have been detected and classified from a video recording captured during the quality control process. Fabric quality control system prototype has been manufactured and a thermal camera was located on the quality control machine. The defective areas on the fabric surface were detected using the heat difference occurring between the defective and defect-free zones. Gray level co-occurrence matrix is used for feature extraction for defective images. The defective images are classified by k-nearest neighbor algorithm. The image processing stage consists of wavelet, threshold, and morphological operations. The defects have been classified with an average accuracy rate of 96%. In addition, the location of the defect has been identified and the defect type and location are recorded during the process via specially designed image processing interface. According to the experimental results, the proposed method works effectively.