Robust fabric defect detection and classification using multiple adaptive wavelets

The wavelet transform has been widely used for defect detection and classification in fabric images. The detection and classification performance of the wavelet transform approach is closely related to the selection of the wavelet. Instead of predetermining a wavelet, a method of designing a wavelet to adapt to the detection or classification of fabric defects has been developed. For further improvement of the performance, the paper extends the adaptive wavelet-based methodology from the use of a single adaptive wavelet to multiple adaptive wavelets. For each class of fabric defect, a defect-specific adaptive wavelet was designed to enhance the defect region at one channel of the wavelet transform, where the defect region can be detected by using a simple threshold classifier. Corresponding to the multiple defect-specific adaptive wavelets, the multiscale edge responses to defect regions have been shown to be more efficient in characterising defects, which leads to a new approach to the classification of defects. In comparison with the single adaptive wavelet approach, the use of multiple adaptive wavelets yields better performance on defect detection and classification, especially for defects that are poorly detected by the single adaptive wavelet approach. The proposed method has been evaluated on the inspection of 56 images containing eight classes of fabric defects, and 64 images without defects. In defect detection, 98.2% detection rate and 1.5% false alarm rate were achieved, and in defect classification, 97.5% accuracy was achieved.