SCL-VI：面向工业缺陷视觉检测的自监督上下文学习
SCL-VI: Self-supervised Context Learning for Visual Inspection of Industrial Defects
AI总结：
针对工业产品缺陷无监督检测难以平衡纹理与物体缺陷的问题，提出SCL-VI自监督算法，通过拼图任务提取语义并结合SVDD算法，在MVTec AD数据集上实现了95.8%检测率和96.8%分割率的最先进性能。
摘要：
由于产品表面存在巨大差异，工业产品缺陷的无监督视觉检测面临重大挑战。当前的无监督模型难以在检测纹理缺陷和物体缺陷之间取得平衡，缺乏辨别潜在表示和复杂特征的能力。本文提出了一种新颖的自监督学习算法，旨在通过解决著名的拼图问题来推导出最优编码器。该方法将目标图像划分为九个图像块，让编码器预测任意两个图像块之间的相对位置关系，以提取丰富的语义信息。随后，引入一种亲和力增强方法来突出正常与异常潜在表示之间的差异。利用经典的support vector data description算法得出最终检测结果。实验结果表明，所提方法在广泛使用的MVTec AD数据集上取得了优异的检测和分割性能，准确率分别达到95.8%和96.8%，为纹理和物体缺陷建立了最先进的基准。综合实验突显了该方法在多种工业应用中的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
