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학술지 Patch-Level Operation With Adaptive Patch Control for Improving Anomaly Localization
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이현용, 김낙우, 이준기, 이병탁
IEEE Access, v.9, pp.90727-90737
21ZK1100, 호남권 지역산업 기반 ICT 융합기술 고도화 지원사업, 이길행
In realizing unsupervised pixel-precise anomaly localization by utilizing a generative model, a reference image must be generated (for comparison with an input image) by transforming abnormal patterns of an input image, if any, into normal patterns. In this study, a patch-level operation with adaptive patch control is proposed to improve anomaly localization by generating a better reference image. As a way to exploit a generative model, we divide an image into non-overlapped patches of the same size, generate patch-level reference images, and stitch the patch-level reference images into a single reference image. We then conduct anomaly localization by comparing an input image with the stitched, reconstructed image. To effectively apply the patch-level operation, we propose adaptive patch control to determine the number of non-overlapped patches to be applied. For this, we synthesize defective images using normal images and examine how well the candidate methods with different numbers of patches remove the synthesized defects. In the same way, we utilize adaptive patch control to select a promising model among the candidate generative models. Based on experiments conducted using the MVTec Anomaly Detection dataset, we demonstrate that our method outperforms previous existing methods. Under a real-world scenario, our method shows ROC AUC of 0.926, in contrast to the best value of 0.893 from existing studies. Furthermore, we prove the feasibility of the adaptive patch control by showing that the removal of the synthesized defects and the anomaly localization for real defective images are highly correlated.
Anomaly localization, deep learning, generative model, patch-level operation, unsupervised learning
KSP 제안 키워드
Anomaly localization, Real-world, Reference Image, adaptive patch, anomaly detection, deep learning(DL), generative models, reconstructed image, unsupervised learning
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저작자 표시 (CC BY)