Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy

TL;DR

This paper introduces EAR, a novel self-supervised anomaly detection method that uses deterministic masking and visual obfuscation to improve defect detection accuracy and efficiency in manufacturing, without changing neural network structures.

Contribution

The paper proposes EAR, a new reconstruction-by-inpainting approach utilizing pre-trained attention and visual obfuscation, addressing key issues in existing methods for manufacturing defect detection.

Findings

Deterministic masking effectively isolates defective regions.

Hint-providing mosaicing improves anomaly detection performance.

The method achieves high accuracy without altering neural network architecture.

Abstract

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.