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Paper Abstract and Keywords
Presentation 2022-02-22 16:45
A Note on Accurate Distress Classification Using Deep Learning Considering Confidence in Attention map
Naoki Ogawa, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) This paper presents a note on accurate distress classification using deep learning considering confidence in attention maps. Specifically, this paper proposes confidence-aware attention branch network (ConfABN), which introduces a confidence-aware attention mechanism that reduces the influence of ineffective attention maps by considering the corresponding confidence in the attention maps. The confidence can be calculated from the entropy of the estimated class probabilities in generating the attention map, and it enables the weighting of feature maps using the effective attention map strongly and the ineffective attention map weakly. ConfABN can effectively utilize the attention mechanism to focus more attention on the regions that are important for the final estimation by considering the confidence. Experiments using images taken during inspections of actual infrastructures verify the effectiveness of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) Distress image / deterioration level estimation / deep learning / confidence / attention map / / /  
Reference Info. ITE Tech. Rep., vol. 46, no. 6, ME2022-58, pp. 371-376, Feb. 2022.
Paper # ME2022-58 
Date of Issue 2022-02-14 (MMS, ME, AIT) 
ISSN Print edition: ISSN 1342-6893    Online edition: ISSN 2424-1970
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Conference Information
Committee AIT ME MMS IEICE-IE IEICE-ITS  
Conference Date 2022-02-21 - 2022-02-22 
Place (in Japanese) (See Japanese page) 
Place (in English) online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ME 
Conference Code 2022-02-AIT-ME-MMS-IE-ITS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Note on Accurate Distress Classification Using Deep Learning Considering Confidence in Attention map 
Sub Title (in English)  
Keyword(1) Distress image  
Keyword(2) deterioration level estimation  
Keyword(3) deep learning  
Keyword(4) confidence  
Keyword(5) attention map  
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1st Author's Name Naoki Ogawa  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Keisuke Maeda  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Takahiro Ogawa  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Miki Haseyama  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker Author-1 
Date Time 2022-02-22 16:45:00 
Presentation Time 15 minutes 
Registration for ME 
Paper # MMS2022-33, ME2022-58, AIT2022-33 
Volume (vol) vol.46 
Number (no) no.6 
Page pp.371-376 
#Pages
Date of Issue 2022-02-14 (MMS, ME, AIT) 


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