Fabric defect detection using saliency of multi‐scale local steering kernel

Abstract

Fabric defect detection (FDD) plays an important role in the quality control in textile industry. In this study, the authors propose an efficient FDD method by using the saliency analysis of multi-scale local steering kernel (LSK). In the proposed method, a given RGB fabric image is first converted into the Commission International Eclairage (CIE) L*a*b colour space and then the LSK in each colour channel is computed by the singular value decomposition and the centre surrounding definition. Next, the matrix cosine similarity is employed to measure the similarity between different LSK features for generating the desired defective maps. Finally, a multi-scale averaging fusion scheme is applied to integrate the obtained defective maps at different scales for the final defective map. The experimental results indicate that the proposed method achieves the state-of-the-art performance on FDD compared to the other competitors.