Stitching defect detection and classification using wavelet transform and BP neural network

In the textile and clothing industry, much research has been conducted on fabric defect automatic detection and classification. However, little research has been done to evaluate specifically the stitching defects of a garment. In this study, a stitching detection and classification technique is presented, which combines the improved thresholding method based on the wavelet transform with the back propagation (BP) neural network. The smooth subimage at a certain resolution level using the pyramid wavelet transform was obtained. The study uses the direct thresholding method, which is based on wavelet transform smooth subimages from the use of a quadrant mean filtering method, to attenuate the texture background and preserve the anomalies. The images are then segmented by thresholding processing and noise filtering. Nine characteristic variables based on the spectral measure of the binary images were collected and input into a BP neural network to classify the sample images. The classification results demonstrate that the proposed method can identify five classes of stitching defects effectively. Comparisons of the proposed new direct thresholding method with the direct thresholding method based on the wavelet transform detailed subimages and the automatic band selection for wavelet reconstruction method were made and the experimental results show that the proposed method outperforms the other two approaches.