Img2PatchSeqAD: Industrial Image Anomaly Detection Based on Image Patch Sequence

Simple item page
dc.contributor.authorLiu, Yangen_US
dc.contributor.authorJi, Ya Tuen_US
dc.contributor.authorXue, Xiangen_US
dc.contributor.authorXu, H. T.en_US
dc.contributor.authorRen, Qing Dao Er Jien_US
dc.contributor.authorShi, Baoen_US
dc.contributor.authorWu, N. E.en_US
dc.contributor.authorLu, M.en_US
dc.contributor.authorXu, Xuan Xuanen_US
dc.contributor.authorGuo, H. X.en_US
dc.contributor.authorWang, L.en_US
dc.contributor.authorDai, L. J.en_US
dc.contributor.authorYao, Miao Miaoen_US
dc.contributor.authorLi, Xiao Meien_US
dc.contributor.editorChen, Renjieen_US
dc.contributor.editorRitschel, Tobiasen_US
dc.contributor.editorWhiting, Emilyen_US
dc.date.accessioned2024-10-13T18:06:07Z
dc.date.available2024-10-13T18:06:07Z
dc.date.issued2024
dc.description.abstractIn 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.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationPacific Graphics Conference Papers and Posters
dc.identifier.doi10.2312/pg.20241324
dc.identifier.isbn978-3-03868-250-9
dc.identifier.pages2 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20241324
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20241324
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies->Image segmentation; Scene anomaly detection
dc.subjectComputing methodologies
dc.subjectImage segmentation
dc.subjectScene anomaly detection
dc.titleImg2PatchSeqAD: Industrial Image Anomaly Detection Based on Image Patch Sequenceen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
pg20241324.pdf
Size:
399.33 KB
Format:
Adobe Portable Document Format
Download
Collections
PG2024 Conference Papers and Posters