Img2PatchSeqAD: Industrial Image Anomaly Detection Based on Image Patch Sequence
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
In the domain of industrial Visual Anomaly Detection(VAD), methods based on image reconstruction are the most popular and successful approaches. However, current image reconstruction methods rely on global image information, which proves to be both blind and inefficient for anomaly detection tasks. Our approach tackles these limitations by taking advantage of neighboring image patches to assess the presence of anomalies in the current image and then selectively reconstructing those patches. In this paper, we introduce a novel architecture for image anomaly detection, named Img2PatchSeqAD. Specifically, we employ a row-wise scanning method to construct sequences of image patches and design a network framework based on an image patch sequence encoder-decoder structure. Additionally, we utilize the KAN model and ELA attention mechanism to develop methods for image patch vectorization and establish an image reconstruction pipeline. Experimental results on the MVTec-AD and VisA datasets demonstrate the effectiveness of our approach, achieving localization and detection scores of 81.3 (AUROC) and 91.9 (AP) on the multi-class MVTec-AD dataset.
Description
CCS Concepts: Computing methodologies->Image segmentation; Scene anomaly detection
@inproceedings{10.2312:pg.20241324,
booktitle = {Pacific Graphics Conference Papers and Posters},
editor = {Chen, Renjie and Ritschel, Tobias and Whiting, Emily},
title = {{Img2PatchSeqAD: Industrial Image Anomaly Detection Based on Image Patch Sequence}},
author = {Liu, Yang and Ji, Ya Tu and Wang, L. and Dai, L. J. and Yao, Miao Miao and Li, Xiao Mei and Xue, Xiang and Xu, H. T. and Ren, Qing Dao Er Ji and Shi, Bao and Wu, N. E. and Lu, M. and Xu, Xuan Xuan and Guo, H. X.},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-250-9},
DOI = {10.2312/pg.20241324}
}