Fabric defect detection via saliency model based on adjacent context coordination and transformer

Abstract

Fabric defect detection is a pivotal step in quality control in the textile manufacturing industry. Due to the diversity and complexity of defects, manual visual inspection and traditional fabric defect detection methods suffer from low efficiency and accuracy. To address the issues, a saliency model capable of mining local and global information from CNN and vision Transformer is proposed for fabric defect detection in this paper, named ACCTNet. Specifically, to enhance the feature interaction of different scales, an adjacent context coordination module composed of one local branch and two adjacent branches is proposed. Meanwhile, a contrast-aggregation module is proposed to highlight the defects from low contrast background using pooling and subtraction operations. In addition, vision Transformer is adopted to capture global contextual information with long-range dependencies, which can guide local information to further refines the defect detection results. Experimental results demonstrate that the proposed method can accurately inspect the defects from plain and patterned fabric surfaces, achieving Em values of 78.49% and 97.19% respectively, which significantly surpasses the existing state-of-the-art fabric defect detection methods.