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The unsupervised visual inspection of defects in industrial products poses asignificant challenge due to substantial variations in product surfaces.Current unsupervised models struggle to strike a balance between detectingtexture and object defects, lacking the capacity to discern latentrepresentations and intricate features. In this paper, we present a novelself-supervised learning algorithm designed to derive an optimal encoder bytackling the renowned jigsaw puzzle. Our approach involves dividing the targetimage into nine patches, tasking the encoder with predicting the relativeposition relationships between any two patches to extract rich semantics.Subsequently, we introduce an affinity-augmentation method to accentuatedifferences between normal and abnormal latent representations. Leveraging theclassic support vector data description algorithm yields final detectionresults. Experimental outcomes demonstrate that our proposed method achievesoutstanding detection and segmentation performance on the widely used MVTec ADdataset, with rates of 95.8% and 96.8%, respectively, establishing astate-of-the-art benchmark for both texture and object defects. Comprehensiveexperimentation underscores the effectiveness of our approach in diverseindustrial applications.

SCL-VI: Self-supervised Context Learning for Visual Inspection of Industrial Defects