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Paper Abstract and Keywords
Presentation 2024-03-15 15:30
High Precision Anomaly Detection using PaDiM based on Pre-training with Normality Constraint of Normal Features
Hiroki Kobayashi, Manabu Hashimoto (Chukyo Univ.) IMQ2023-89 IE2023-144 MVE2023-118
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, automatic visual inspection is expected with machine learning. Among them, PaDiM is attracting attention as superior anomaly detection. PaDiM has the following three steps: (1) Pre-training of the network using ImageNet, (2) Normal distribution approximation of features for normal images at actual factory, (3) Anomaly detection using Mahalanobis distance using normal distribution parameters. PaDiM assumes that normal features after pre-training follow normal distribution. However, the model is pre-trained without normal images of actual factory, then these normal features may not follow normal distribution. Therefore, in this research, we proposed the highly precision anomaly detection that pre-train the model with normality constraint of normal features. In MVTecAD experiments, the performance improved from 87.9% with the previous method to 93.3% with the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Visual inspection / Anomaly detection / PaDiM / Pre-training / Normality constraint / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 432, IE2023-144, pp. 408-413, March 2024.
Paper # IE2023-144 
Date of Issue 2024-03-06 (IMQ, IE, MVE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IMQ2023-89 IE2023-144 MVE2023-118

Conference Information
Committee IE MVE CQ IMQ  
Conference Date 2024-03-13 - 2024-03-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Sangyo Shien Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Media of five senses, Multimedia, Media experience, Picture codinge, Image media quality, Network,quality and reliability, etc（AC） 
Paper Information
Registration To IE 
Conference Code 2024-03-IE-MVE-CQ-IMQ 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) High Precision Anomaly Detection using PaDiM based on Pre-training with Normality Constraint of Normal Features 
Sub Title (in English)  
Keyword(1) Visual inspection  
Keyword(2) Anomaly detection  
Keyword(3) PaDiM  
Keyword(4) Pre-training  
Keyword(5) Normality constraint  
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1st Author's Name Hiroki Kobayashi  
1st Author's Affiliation Chukyo University (Chukyo Univ.)
2nd Author's Name Manabu Hashimoto  
2nd Author's Affiliation Chukyo University (Chukyo Univ.)
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Speaker Author-1 
Date Time 2024-03-15 15:30:00 
Presentation Time 20 minutes 
Registration for IE 
Paper # IMQ2023-89, IE2023-144, MVE2023-118 
Volume (vol) vol.123 
Number (no) no.430(IMQ), no.432(IE), no.433(MVE) 
Page pp.408-413 
#Pages 6 
Date of Issue 2024-03-06 (IMQ, IE, MVE) 

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