  Due to scarcity of anomaly situations in the early manufacturing stage, an
unsupervised anomaly detection (UAD) approach is widely adopted which only uses
normal samples for training. This approach is based on the assumption that the
trained UAD model will accurately reconstruct normal patterns but struggles
with unseen anomalous patterns. To enhance the UAD performance,
reconstruction-by-inpainting based methods have recently been investigated,
especially on the masking strategy of suspected defective regions. However,
there are still issues to overcome: 1) time-consuming inference due to multiple
masking, 2) output inconsistency by random masking strategy, and 3) inaccurate
reconstruction of normal patterns when the masked area is large. Motivated by
this, we propose a novel reconstruction-by-inpainting method, dubbed Excision
And Recovery (EAR), that features single deterministic masking based on the
ImageNet pre-trained DINO-ViT and visual obfuscation for hint-providing.
Experimental results on the MVTec AD dataset show that deterministic masking by
pre-trained attention effectively cuts out suspected defective regions and
resolve the aforementioned issues 1 and 2. Also, hint-providing by mosaicing
proves to enhance the UAD performance than emptying those regions by binary
masking, thereby overcomes issue 3. Our approach achieves a high UAD
performance without any change of the neural network structure. Thus, we
suggest that EAR be adopted in various manufacturing industries as a
practically deployable solution.
