Saliency-based fabric defect detection network with feature pyramid learning and refinement

Abstract

Fabric defect detection is an essential step of quality control in the textile manufacturing industry. The fabric image texture and defects are complex and diverse, which result in poor detection results and low efficiency of the traditional fabric defect detection algorithm. Visual saliency model can quickly outstand the salient object from the complex background, and has been proven applicable in fabric defect detection. However, the existing saliency detection models still confront great challenges in boundary refinement and line-shaped defect detection. Therefore, a novel saliency-based fabric defect detection network with feature pyramid learning and refinement module is proposed to powerfully characterize features and refine boundary, in which a scale-correlated feature pyramid module (SCFPM) with cross-level connections is proposed to effectively characterize the multi-scale features from the backbone network. Moreover, an auxiliary refinement module (ARM) is designed to further refine and strengthen the input features. Finally, we incorporated the hand-crafted saliency priors to guide the network to generate the accurate saliency maps. Extensive experiments on the built fabric image datasets demonstrate that our proposed model outperforms most state-of-the-art methods under different evaluation metrics.