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Paper Abstract and Keywords
Presentation 2026-03-19 09:50
Enhancement of a Deep Learning-Based Visual Inspection Method for Sewing Machine Needles
Shingo Tomonaga (Kumamoto IRI), Kim Wonjik (AIST) MSS2025-66 NLP2025-147
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents a deep-learning-based visual inspection method for sewing needles, with the aim of achieving both high inspection accuracy and computational efficiency. The proposed approach introduces a multi-channel input representation by concatenating images captured sequentially from nine different viewpoints along the channel dimension, and applies a lightweight CNN model to detect defective products. To address the limited availability of defective samples in manufacturing environments, model performance is evaluated using cross-validation, and the prediction tendencies of the model are analyzed through Grad-CAM visualization. In addition, inference time is measured to verify that the proposed method can operate within the takt time of actual manufacturing lines, even in a CPU-based environment. These results indicate that the proposed approach is a practical visual inspection solution for manufacturing sites operating under limited-data conditions.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Learning / CNN Models / Visual Inspection / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 418, NLP2025-147, pp. 104-107, March 2026.
Paper # NLP2025-147 
Date of Issue 2026-03-11 (MSS, NLP) 
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 MSS2025-66 NLP2025-147

Conference Information
Committee MSS NLP  
Conference Date 2026-03-18 - 2026-03-19 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To NLP 
Conference Code 2026-03-MSS-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Enhancement of a Deep Learning-Based Visual Inspection Method for Sewing Machine Needles 
Sub Title (in English)  
Keyword(1) Deep Learning  
Keyword(2) CNN Models  
Keyword(3) Visual Inspection  
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1st Author's Name Shingo Tomonaga  
1st Author's Affiliation Kumamoto Industrial Research Institute (Kumamoto IRI)
2nd Author's Name Kim Wonjik  
2nd Author's Affiliation National Institute of Advanced Industrial Science and Technology (AIST)
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Speaker Author-1 
Date Time 2026-03-19 09:50:00 
Presentation Time 20 minutes 
Registration for NLP 
Paper # MSS2025-66, NLP2025-147 
Volume (vol) vol.125 
Number (no) no.417(MSS), no.418(NLP) 
Page pp.104-107 
#Pages 4 
Date of Issue 2026-03-11 (MSS, NLP) 

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