AAAD: Adaptive Activated Anomaly Detection on Varied Backgrounds

Ken Miyamoto, Teng-Yok Lee, Akira Minezawa

Abstract: Visual anomaly detection is advantageous for typical industrial applications due to its minimal training data requirements. However, most visual anomaly detection methods assume a fixed background. This assumption leads to performance degradation on varied backgrounds. One primary cause is the misclassification of untrained backgrounds as anomalies. To mitigate the influence of varied backgrounds, we propose Adaptive Activated Anomaly Detection (AAAD), which distinguishes between the inspection target and backgrounds. AAAD incorporates segmentation with contrastive learning to emphasize subtle differences between the inspection target and backgrounds. To minimize the load of annotation for the additional segmentation task, weakly few-shot labeled images are used. Evaluation on an industrial dataset with varied backgrounds demonstrates that the pixel-level AUROC of AAAD significantly surpass those of conventional methods.

External IDs:dblp:conf/avss/MiyamotoLM25