Fabric Stitching Inspection Using Segmented Window Technique and BP Neural Network

In the textile and clothing industry, much research has been conducted on fabric defect automatic detection. However, few have been specifically designed for evaluating fabric stitches or seams of semi-finished and finished garments. In this paper, a fabric stitching inspection method is proposed for knitted fabric in which a segmented window technique is developed to segment images into three classes using a monochrome single-loop ribwork of knitted fabric: (1) seams without sewing defects; (2) seams with pleated defects; and (3) seams with puckering defects caused by stitching faults. Nine characteristic variables were obtained from the segmented images and input into a Back Propagation (BP) neural network for classification and object recognition. The classification results demonstrate that the inspection method developed is effective in identifying the three classes of knitted-fabric stitching. It is proved that the classifier with nine characteristic variables outperformed those with five and seven variables and the neural network technique using either BP or radial basis (RB) is effective for classifying the fabric stitching defects. By using the BP neural network, the recognition rate was 100%.