注意力模块提升工业检测中的图像级异常检测：一项DifferNet案例研究
Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study
AI总结：
本研究针对工业视觉检测中缺陷样本少、标注数据不足的问题，提出融合注意力模块的AttentDifferNet方法，在三个工业异常检测数据集上性能优于基线DifferNet，在真实场景数据集达SOTA，为相关研究提供基准。
摘要：
在（半）自动化视觉工业检测中，包括深度神经网络在内的基于学习的视觉缺陷评估方法，能够处理高分辨率图像上像素级的微小缺陷图案。这类缺陷图案通常很少出现，这一现状解释了对标注数据集的普遍需求。为缓解这一问题并推动无监督视觉检测的当前技术水平，本研究提出一种增强了注意力模块的基于DifferNet的解决方案：AttentDifferNet。该方法在三个用于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性能。与现有技术相比，AttentDifferNet取得了更优的结果，这些结果在我们的定性-定量研究中得到了重点展示。我们的定量评估显示，在全部三个数据集上，与DifferNet相比，AttentDifferNet的整体AUROC平均提升了1.77 ± 0.25个百分点，在真实场景工业检测数据集InsPLAD-fault上达到了SOTA结果。由于AttentDifferNet的变体在当前研究的方法中展现出巨大潜力，本文制定了一个基准，强调了注意力机制在真实场景和受控环境下的工业异常检测中的重要性。
英文摘要：
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.
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。
