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Paper Abstract and Keywords
Presentation 2026-02-20 10:30
[Special Talk] Anomaly Detection Using Semantic Segmentation Model and Large Vision-Language Model for Efficient Daily Inspection on Highways
Ren Tasai, Xiang Li, Ryota Goka, Naoki Saito, Keisuke Maeda (Hokkaido Univ.), Fumiyuki Kamada (Nexco-Engineering Hokkaido), Ryushi Kubo (NEXCO-East Engineering), Yuji Kawasaki (East Nippon Expressway Kanto Regional Head Office), Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) This paper proposes an anomaly detection method for road facilities based on a semantic segmentation model and a vision language model, aiming to improve the efficiency of routine inspections on expressways. In the proposed method, cropped images limited to a single target object are obtained from input images by applying semantic segmentation–based cropping. Furthermore, by introducing in-context learning with a small number of normal and anomalous cropped images, anomaly detection can be achieved without additional training. By focusing on cropped images of the target object, the proposed method reduces the influence of visual information irrelevant to the object and improves anomaly detection performance. Experiments using frame images extracted from in-vehicle camera videos captured on actual expressways confirm the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) semantic segmentation / vision language model / in-vehicle camera footage / anomaly detection / / / /  
Reference Info. ITE Tech. Rep., vol. 50, no. 5, ME2026-24, pp. 101-105, Feb. 2026.
Paper # ME2026-24 
Date of Issue 2026-02-12 (MMS, ME, AIT, SIP) 
ISSN Online edition: ISSN 2424-1970
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Conference Information
Committee IEICE-IE IEICE-ITS MMS ME AIT SIP  
Conference Date 2026-02-19 - 2026-02-20 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ME 
Conference Code 2026-02-IE-ITS-MMS-ME-AIT-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Anomaly Detection Using Semantic Segmentation Model and Large Vision-Language Model for Efficient Daily Inspection on Highways 
Sub Title (in English)  
Keyword(1) semantic segmentation  
Keyword(2) vision language model  
Keyword(3) in-vehicle camera footage  
Keyword(4) anomaly detection  
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1st Author's Name Ren Tasai  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Xiang Li  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Ryota Goka  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Naoki Saito  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
5th Author's Name Keisuke Maeda  
5th Author's Affiliation Hokkaido University (Hokkaido Univ.)
6th Author's Name Fumiyuki Kamada  
6th Author's Affiliation Nexco-Engineering Hokkaido Company Limited (Nexco-Engineering Hokkaido)
7th Author's Name Ryushi Kubo  
7th Author's Affiliation NEXCO-East Engineering Company Limited (NEXCO-East Engineering)
8th Author's Name Yuji Kawasaki  
8th Author's Affiliation East Nippon Expressway Company Limited Kanto Regional Head Office (East Nippon Expressway Kanto Regional Head Office)
9th Author's Name Takahiro Ogawa  
9th Author's Affiliation Hokkaido University (Hokkaido Univ.)
10th Author's Name Miki Haseyama  
10th Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker Author-1 
Date Time 2026-02-20 10:30:00 
Presentation Time 10 minutes 
Registration for ME 
Paper # MMS2026-24, ME2026-24, AIT2026-24, SIP2026-24 
Volume (vol) vol.50 
Number (no) no.5 
Page pp.101-105 
#Pages 5 
Date of Issue 2026-02-12 (MMS, ME, AIT, SIP) 

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