Category-Agnostic Cluster-Guided Selective Suppression for Weakly Supervised Surface Defect Localization

Surface defect localization is indispensable for the quality control of industrial products in the manufacturing process, supervised dense prediction of defect areas requires laborious and difficult pixel-level annotations. On the contrary, weakly supervised defect localization (WSDL) is much more practicable, and the class activation maps (CAMs) generated with image-level labels can serve as prior knowledge or cues for downstream tasks. However, due to interference of unforeseen background objects that may have high confidence in activation maps instead, the localization maps obtained from general WSDL methods are prone to be unreliable, which leads to errors or even missing localization. Therefore, in this article, we innovatively propose a simple, yet effective cluster-guided selective suppression (CSS) strategy for weakly supervised surface defect localization, aiming at the rectification of CAMs, thus producing more reliable localization maps. Specifically, CSS leverages converted rectangle patch pairs generated from CAMs and performs unsupervised vector-level cluster analysis, all patches are classified into defective or nondefective clusters. Subsequently, a category-agnostic cluster is selected to provide suppression feedback for the image suppressor, and the suppressed images are recycled for feature re-learning. Extensive experimental results demonstrate that our proposed method achieves state-of-the-art WSDL performance on three public datasets of metal and magnetic-tile surfaces, with average classification and localization accuracy improved by 7.19% and 5.40%, respectively.