The unsupervised visual inspection of defects in industrial products poses a
significant challenge due to substantial variations in product surfaces.
Current unsupervised models struggle to strike a balance between detecting
texture and object defects, lacking the capacity to discern latent
representations and intricate features. In this paper, we present a novel
self-supervised learning algorithm designed to derive an optimal encoder by
tackling the renowned jigsaw puzzle. Our approach involves dividing the target
image into nine patches, tasking the encoder with predicting the relative
position relationships between any two patches to extract rich semantics.
Subsequently, we introduce an affinity-augmentation method to accentuate
differences between normal and abnormal latent representations. Leveraging the
classic support vector data description algorithm yields final detection
results. Experimental outcomes demonstrate that our proposed method achieves
outstanding detection and segmentation performance on the widely used MVTec AD
dataset, with rates of 95.8% and 96.8%, respectively, establishing a
state-of-the-art benchmark for both texture and object defects. Comprehensive
experimentation underscores the effectiveness of our approach in diverse
industrial applications.

本文提出了一种新颖的自监督学习算法，通过解决著名的拼图问题来设计一个优化的编码器，以在工业产品中检测纹理和物体缺陷，实验结果表明我们的方法在广泛使用的 MVTec AD 数据集上达到了 95.8% 和 96.8% 的杰出检测和分割性能，为纹理和物体缺陷建立了最新的基准。