Mask-patchcore: a robust anomaly detection model focusing on interested region

Fan Wu; Shuchang Xu

doi:10.1117/12.3057825

13 February 2025 Mask-patchcore: a robust anomaly detection model focusing on interested region

Fan Wu, Shuchang Xu

Author Affiliations +

Proceedings Volume 13539, Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024); 135390B (2025) https://doi.org/10.1117/12.3057825
Event: Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024), 2024, Nanjing, China

Abstract

Background noise can easily lead to incorrect classification in anomaly detection, which is a crucial step in industrial product quality inspection. In this paper, we introduce Mask-Patchcore, a simple yet effective algorithm designed to mitigate the impact of background noise. Firstly, we employ a segmentation-based mask generator that integrates with the input image in the Mask-Patchcore detection network. This approach allows for the assignment of varying weights to different segments of the image, thereby enhancing focus on regions of interest. By dynamically adjusting attention across image regions via the mask generator, Mask-Patchcore significantly improves the anomaly detection algorithm’s ability to pinpoint and identify target areas. This enhancement boosts overall detection accuracy and robustness. We extensively evaluate Mask-Patchcore using public datasets such as MVTEC-AD, VISA, and MPDD. Experimental results demonstrate its superior performance compared to existing algorithms. Furthermore, we showcase its successful application in detecting map boundary anomalies.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fan Wu and Shuchang Xu "Mask-patchcore: a robust anomaly detection model focusing on interested region", Proc. SPIE 13539, Sixteenth International Conference on Graphics and Image Processing (ICGIP 2024), 135390B (13 February 2025); https://doi.org/10.1117/12.3057825

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