Within (semi-)automated visual industrial inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery. The emergence of these often rarely occurring defect patterns explains the general need for labeled data corpora. To alleviate this issue and advance the current state of the art in unsupervised visual inspection, this work proposes a DifferNet-based solution enhanced with attention modules: AttentDifferNet. It improves image-level detection and classification capabilities on three visual anomaly detection datasets for industrial inspection: InsPLAD-fault, MVTec AD, and Semiconductor Wafer. In comparison to the state of the art, AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quali-quantitative study. Our quantitative evaluation shows an average improvement - compared to DifferNet - of 1.77 +/- 0.25 percentage points in overall AUROC considering all three datasets, reaching SOTA results in InsPLAD-fault, an industrial inspection in-the-wild dataset. As our variants to AttentDifferNet show great prospects in the context of currently investigated approaches, a baseline is formulated, emphasizing the importance of attention for industrial anomaly detection both in the wild and in controlled environments.

翻译：在（半）自动化视觉工业检测中，基于学习的方法（包括深度神经网络）用于评估视觉缺陷，能够处理高分辨率图像中以像素尺寸呈现的微小缺陷模式。这些通常罕见的缺陷模式的出现解释了标注数据集的普遍需求。为解决这一问题并推动无监督视觉检测领域的最新进展，本工作提出了一种基于DifferNet并增强注意力模块的解决方案：AttentDifferNet。该方案在三个工业检测的视觉异常检测数据集（InsPLAD-fault、MVTec AD和Semiconductor Wafer）上提升了图像级检测与分类能力。与现有最先进方法相比，AttentDifferNet取得了更优的结果，这些结果在我们的定性与定量联合研究中得到了突出展示。定量评估表明，相较于DifferNet，在综合考虑三个数据集时，AttentDifferNet的整体AUROC平均提升了1.77 ± 0.25个百分点，并在工业野外检测数据集InsPLAD-fault上达到了最先进水平。由于AttentDifferNet的变体在当前研究的方法中展现出巨大潜力，我们提出了一项基线，强调了注意力机制在工业异常检测（无论是野外环境还是受控环境）中的重要性。