Modeling of fabric sewing break detection based on U-Net network

To solve the problem of false positives and false negatives in the manual detection of fabric sewing breaks, a method of fabric sewing break detection based on the U-Net network is proposed. By detecting the adjacent distance between the characteristic contours of adjacent sewing stitches, the distribution uniformity of sewing stitches in sewing patterns is calculated, and the abnormal detection and traceability of fabric sewing broken threads are realized. First, the U-Net network sewing feature extraction model was trained using sewing images and their corresponding stitching feature annotation maps. Then, the trained network model was used to process sewing image samples to obtain binary stitching feature maps. Second, the stitching feature maps were processed using a closing operation to eliminate residual image noise. On this basis, the template matching algorithm was used to extract the stitching feature contours. Finally, according to the distance between adjacent feature contours, the fabric sewing break detection and abnormality tracing model was constructed. The model is validated by examples, and the results show that the abnormal samples of stitching lines are detected, and the corresponding break positions are given. The overall detection accuracy of the model is 95.75%, indicating that the constructed fabric sewing break detection model is effective.