Deterioration Level Estimation Based on Convolutional Neural Network Using Confidence-Aware Attention Mechanism for Infrastructure Inspection

Ogawa, Naoki; Maeda, Keisuke; Ogawa, Takahiro; Haseyama, Miki

Deterioration Level Estimation Based on Convolutional Neural Network Using Confidence-Aware Attention Mechanism for Infrastructure Inspection

Ogawa, Naoki; Maeda, Keisuke; Ogawa, Takahiro et al.

Sensors, 2022, 22(1), 382

Permalink : https://hdl.handle.net/2115/84047

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https://doi.org/10.3390/s22010382			15

論文情報

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アクセス権	metadata only access
URI	https://hdl.handle.net/2115/84047
タイトル	Deterioration Level Estimation Based on Convolutional Neural Network Using Confidence-Aware Attention Mechanism for Infrastructure Inspection
著者	Ogawa, Naoki ; Maeda, Keisuke ; Ogawa, Takahiro ; Haseyama, Miki
著者名 Ogawa, Naoki
著者名 Maeda, Keisuke
e-Rad_Researcher 20524028 著者名 Ogawa, Takahiro
e-Rad_Researcher 00218463 著者名 Haseyama, Miki
言語	英語
キーワード	deterioration level estimation
	convolutional neural network
	attention map
	confidence
	infrastructure inspection
発行日	2022-01
出版者	MDPI
収録物名	Sensors
巻(号)	22(1)
ページ	382
抄録	This paper presents deterioration level estimation based on convolutional neural networks using a confidence-aware attention mechanism for infrastructure inspection. Spatial attention mechanisms try to highlight the important regions in feature maps for estimation by using an attention map. The attention mechanism using an effective attention map can improve feature maps. However, the conventional attention mechanisms have a problem as they fail to highlight important regions for estimation when an ineffective attention map is mistakenly used. To solve the above problem, this paper introduces the confidence-aware attention mechanism that reduces the effect of ineffective attention maps by considering the confidence corresponding to the attention map. The confidence is calculated from the entropy of the estimated class probabilities when generating the attention map. Because the proposed method can effectively utilize the attention map by considering the confidence, it can focus more on the important regions in the final estimation. This is the most significant contribution of this paper. The experimental results using images from actual infrastructure inspections confirm the performance improvement of the proposed method in estimating the deterioration level.
資源タイプ	学術雑誌論文
出版タイプ	Version of Record
関連情報 (isIdenticalTo)	出版社版DOI https://doi.org/10.3390/s22010382