Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Anomaly Detection InsPLAD AttentDifferNet (SENet-AlexNet) Detection AUROC 94.34 # 1
Anomaly Detection InsPLAD RD++ (SENet-ResNet-18) Detection AUROC 90.32 # 4
Anomaly Detection InsPLAD RD++ (CBAM-ResNet-18) Detection AUROC 90.75 # 3