Bi-Grid Reconstruction for Image Anomaly Detection

• URL: http://arxiv.org/abs/2504.00609v1
• Date: Tue, 01 Apr 2025 10:06:38 GMT
• Title: Bi-Grid Reconstruction for Image Anomaly Detection
• Authors: Huichuan Huang, Zhiqing Zhong, Guangyu Wei, Yonghao Wan, Wenlong Sun, Aimin Feng,
• Abstract summary: This paper introduces textbfGRAD: Bi-textbfGrid textbfReconstruction for Image textbfAnomaly textbfDetection.<n>It employs two continuous grids to enhance anomaly detection from both normal and abnormal perspectives.<n>It excels in overall accuracy and in discerning subtle differences, demonstrating its superiority over existing methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In image anomaly detection, significant advancements have been made using un- and self-supervised methods with datasets containing only normal samples. However, these approaches often struggle with fine-grained anomalies. This paper introduces \textbf{GRAD}: Bi-\textbf{G}rid \textbf{R}econstruction for Image \textbf{A}nomaly \textbf{D}etection, which employs two continuous grids to enhance anomaly detection from both normal and abnormal perspectives. In this work: 1) Grids as feature repositories that improve generalization and mitigate the Identical Shortcut (IS) issue; 2) An abnormal feature grid that refines normal feature boundaries, boosting detection of fine-grained defects; 3) The Feature Block Paste (FBP) module, which synthesizes various anomalies at the feature level for quick abnormal grid deployment. GRAD's robust representation capabilities also allow it to handle multiple classes with a single model. Evaluations on datasets like MVTecAD, VisA, and GoodsAD show significant performance improvements in fine-grained anomaly detection. GRAD excels in overall accuracy and in discerning subtle differences, demonstrating its superiority over existing methods.

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