異種特徴間の相関およびAttention Mapの確信度を考慮可能な変状画像の劣化レベル分類
インフラ構造物の維持管理支援のために，変状画像からその進行度合いを分類する深層学習に関する研究が広く行われている．変状画像の分類では，被写体とカメラの距離が画像毎に大きく異なることや被写体自体の多様性等，実データ特有の複雑さにより，深層学習モデルが分類対象と関連しない領域に注目する可能性が高まる．そこで，本稿では，従来の画像のみを用いた深層学習に，変状の発生部位や部材等のテキストデータを導入し，変状領域に注目可能なマルチモーダル深層学習モデルを構築する．さらに，モデルがどの程度の自信を持って変状領域に注目したかを表す確信度を算出し，確信度の高い注目領域が変状分類へ与える影響を強めることで，分類...
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| Published in | AI・データサイエンス論文集 Vol. 3; no. J2; pp. 704 - 713 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
公益社団法人 土木学会
2022
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 2435-9262 |
| DOI | 10.11532/jsceiii.3.J2_704 |
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| Abstract | インフラ構造物の維持管理支援のために，変状画像からその進行度合いを分類する深層学習に関する研究が広く行われている．変状画像の分類では，被写体とカメラの距離が画像毎に大きく異なることや被写体自体の多様性等，実データ特有の複雑さにより，深層学習モデルが分類対象と関連しない領域に注目する可能性が高まる．そこで，本稿では，従来の画像のみを用いた深層学習に，変状の発生部位や部材等のテキストデータを導入し，変状領域に注目可能なマルチモーダル深層学習モデルを構築する．さらに，モデルがどの程度の自信を持って変状領域に注目したかを表す確信度を算出し，確信度の高い注目領域が変状分類へ与える影響を強めることで，分類の高精度化を実現する．本稿の最後では，実際の変状画像を用いた実験により提案手法の有効性を検証する． |
|---|---|
| AbstractList | インフラ構造物の維持管理支援のために，変状画像からその進行度合いを分類する深層学習に関する研究が広く行われている．変状画像の分類では，被写体とカメラの距離が画像毎に大きく異なることや被写体自体の多様性等，実データ特有の複雑さにより，深層学習モデルが分類対象と関連しない領域に注目する可能性が高まる．そこで，本稿では，従来の画像のみを用いた深層学習に，変状の発生部位や部材等のテキストデータを導入し，変状領域に注目可能なマルチモーダル深層学習モデルを構築する．さらに，モデルがどの程度の自信を持って変状領域に注目したかを表す確信度を算出し，確信度の高い注目領域が変状分類へ与える影響を強めることで，分類の高精度化を実現する．本稿の最後では，実際の変状画像を用いた実験により提案手法の有効性を検証する． |
| Author | 小川, 直輝 長谷山, 美紀 前田, 圭介 小川, 貴弘 |
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International Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016. 2) American Water Works Association: State of the water industry, 2021, from https://www.awwa.org/Portals/0/Awwa/Professional%20Development/SOTWI_2021_Full_Report.pdf, (accessed 2022-06-28). 7) L. Yang, B. Li, W. Li, Z. Liu, G. Yang, and J. Xiao: Deep concrete inspection using unmanned aerial vehicle towards cssc database, iProc. International Conference on Intelligent Robots and Systems, pp.24-28, 2017. 19) S. Woo, J. Park, J. Lee, and I.S. Kweon: Cbam: Convolutional block attention module, Proc. European conference on computer vision, pp.3-19, 2018. 16) J. Hu, L. Shen, and G. Sun: Squeeze-and-excitation networks, Proc. International Conference on Computer Vision and Pattern Recognition, pp.7132-7141, 2018. 15) M. Guo, T. Xu, J. Liu, Z. Liu, P. Jiang, T. Mu, S. Zhang, R.R. Martin, M. Cheng, and S. Hu: Attention mechanisms in computer vision: A survey, arXiv preprint arXiv:2111.07624, pp.1-27, 2021. 5) Andrea D’Ariano，L. Meng，G. Centulio，F. Corman: Integrated stochastic optimization approaches for tactical scheduling of trains and railway infrastructure maintenance, Computers & Industrial Engineering, Vol.127， pp.1315-1335, 2019. 10) K. Maeda, S. Takahashi, T. Ogawa, and M. Haseyama: Convolutional sparse coding-based deep random vector functional link network for distress classification of road structures, Computer-Aided Civil and Infrastructure Engineering, Vol.34, No.8, pp.654-676, 2019. 22) M. Mitsuhara, H. Fukui, Y. Sakashita, T. Ogata, T. Hirakawa, T. Yamashita, and H. Fujiyoshi: Embedding human knowledge into deep neural network via attention map, arXiv preprint arXiv:1905.03540, pp.1-10, 2019. 12) A. Krizhevsky, I. Sutskever, and G.E. Hinton: Imagenet classification with deep convolutional neural networks, Proc. Advances in neural information processing systems, pp.1097-1105, 2012. 13) M. Everingham, S. Eslami, L. Van Gool, C.K. Williams, J. Winn, and A. Zisserman: The pascal visual object classes challenge: A retrospective, International journal of computer vision, Vol.111, No.1, pp.98-136, 2015. 28) B. Settles: Active learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, pp.1-65, 2009. 26) K. Maeda, S. Takahashi, T. Ogawa, and M. Haseyama: Estimation of deterioration levels of transmission towers via deep learning maximizing canonical correlation between heterogeneous features, IEEE Journal of Selected Topics in Signal Processing, Vol.12, No.4, pp.633-644, 2018. 9) 国土交通省：令和 4 年版 国土交通白書, 2022, from https://www.mlit.go.jp/hakusyo/mlit/r01/hakusho/r02/pdfindex.html, (accessed 2022-06-01). [Ministry of land, infrastructure, transport and tourism: Reiwa 4 nendo white paper on land, infrastructure, transport and tourism in Japan, 2022.] 1) T. Park, B. Kim, and H. Kim: Impact of deterioration and negotiation on sewer system o&m contracts from the real option perspective, Water resources management, Vol.26, No.10, pp.2973-2989, 2012. 17) K. Li, Z. Wu, K. Peng, J. Ernst, and Y. Fu: Tell me where to look: Guided attention inference network, Proc. International Conference on Computer Vision and Pattern Recognition, pp.9215-9223, 2018. 14) A. Krizhevsky: Learning multiple layers of features from tiny images, Citeseer, pp.1-58, 2009. 6) S. Li and M. Pozzi: What makes long-term monitoring convenient? a parametric analysis of value of information in infrastructure maintenance, Structural Control and Health Monitoring, Vol.26, No.5, p.e2329, 2019. 25) Y. Yeh, C. Huang, and Y. Wang: Heterogeneous domain adaptation and classification by exploiting the correlation subspace, IEEE Transactions on Image Processing, Vol.23, No.5, pp.2009-2018, 2014. 30) N. Ogawa, K. Maeda, T. Ogawa, and M. Haseyama: Deterioration level estimation based on convolutional neural network using confidence-aware attention mechanism for infrastructure inspection, Sensors, Vol.22, No.1, p.382, 2022. 3) American Society of Civil Engineers: A comprehensive assessment of america’s infrastructure, 2021, from https://infrastructurereportcard.org/wp-content/uploads/2020/12/National_IRC_2021-report-2.pdf, (accessed 2022-02-02). 18) J. Park, S. Woo, J. Lee, and I.S. Kweon: Bam: Bottleneck attention module, arXiv preprint arXiv:1807.06514, pp.1-14, 2018. 29) J. Moon, J. Kim, Y. Shin, and S. Hwang: Confidenceaware learning for deep neural networks, Proc. International Conference on Machine Learning, pp.7034-7044 2020. 8) N. Shaghlil and A. Khalafallah: Automating highway infrastructure maintenance using unmanned aerial vehicles, Construction Research Congress, pp.2-4, 2018. 24) K. Maeda, S. Takahashi, T. Ogawa, and M. Haseyama: Distress classification of road structures via adaptive bayesian network model selection, Journal of Computing in Civil Engineering, Vol.31, No.5, pp.04017044_1-04017044_13, 2017. 20) K.H. Lee, C. Park, J. Oh, and N. Kwak: Lfi-cam: Learning feature importance for better visual explanation, Proc. International Conference on Computer Vision, pp.1355-1363, 2021. 21) H. Fukui, T. Hirakawa, T. Yamashita, and H. Fujiyoshi: Attention branch network: Learning of attention mechanism for visual explanation, Proc. International Conference on Computer Vision and Pattern Recognition, pp.10705-10714, 2019. 4) Conference of European Directors of Roads: Re-gen risk assessment of ageing infrastructure risk optimization in road infrastructure elements, 2016. from https://www.cedr.eu/download/other_public_files/research_programme/call_2013/ageing_infrastructure/regen/D5.2-Final-report-on-optimization-of-managementstrategies-under-different-traffic-climate-change-and-financial-scenarios.pdf, (accessed 2022-02-03). |
| References_xml | – reference: 18) J. Park, S. Woo, J. Lee, and I.S. Kweon: Bam: Bottleneck attention module, arXiv preprint arXiv:1807.06514, pp.1-14, 2018. – reference: 21) H. Fukui, T. Hirakawa, T. Yamashita, and H. Fujiyoshi: Attention branch network: Learning of attention mechanism for visual explanation, Proc. International Conference on Computer Vision and Pattern Recognition, pp.10705-10714, 2019. – reference: 14) A. Krizhevsky: Learning multiple layers of features from tiny images, Citeseer, pp.1-58, 2009. – reference: 1) T. Park, B. Kim, and H. Kim: Impact of deterioration and negotiation on sewer system o&m contracts from the real option perspective, Water resources management, Vol.26, No.10, pp.2973-2989, 2012. – reference: 17) K. Li, Z. Wu, K. Peng, J. Ernst, and Y. Fu: Tell me where to look: Guided attention inference network, Proc. International Conference on Computer Vision and Pattern Recognition, pp.9215-9223, 2018. – reference: 3) American Society of Civil Engineers: A comprehensive assessment of america’s infrastructure, 2021, from https://infrastructurereportcard.org/wp-content/uploads/2020/12/National_IRC_2021-report-2.pdf, (accessed 2022-02-02). – reference: 10) K. Maeda, S. Takahashi, T. Ogawa, and M. Haseyama: Convolutional sparse coding-based deep random vector functional link network for distress classification of road structures, Computer-Aided Civil and Infrastructure Engineering, Vol.34, No.8, pp.654-676, 2019. – reference: 9) 国土交通省：令和 4 年版 国土交通白書, 2022, from https://www.mlit.go.jp/hakusyo/mlit/r01/hakusho/r02/pdfindex.html, (accessed 2022-06-01). [Ministry of land, infrastructure, transport and tourism: Reiwa 4 nendo white paper on land, infrastructure, transport and tourism in Japan, 2022.] – reference: 31) K. He, X. Zhang, S. Ren, and J. Sun: Deep residual learning for image recognition, Proc. International Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016. – reference: 8) N. Shaghlil and A. Khalafallah: Automating highway infrastructure maintenance using unmanned aerial vehicles, Construction Research Congress, pp.2-4, 2018. – reference: 4) Conference of European Directors of Roads: Re-gen risk assessment of ageing infrastructure risk optimization in road infrastructure elements, 2016. from https://www.cedr.eu/download/other_public_files/research_programme/call_2013/ageing_infrastructure/regen/D5.2-Final-report-on-optimization-of-managementstrategies-under-different-traffic-climate-change-and-financial-scenarios.pdf, (accessed 2022-02-03). – reference: 22) M. Mitsuhara, H. Fukui, Y. Sakashita, T. Ogata, T. Hirakawa, T. Yamashita, and H. Fujiyoshi: Embedding human knowledge into deep neural network via attention map, arXiv preprint arXiv:1905.03540, pp.1-10, 2019. – reference: 19) S. Woo, J. Park, J. Lee, and I.S. Kweon: Cbam: Convolutional block attention module, Proc. European conference on computer vision, pp.3-19, 2018. – reference: 15) M. Guo, T. Xu, J. Liu, Z. Liu, P. Jiang, T. Mu, S. Zhang, R.R. Martin, M. Cheng, and S. Hu: Attention mechanisms in computer vision: A survey, arXiv preprint arXiv:2111.07624, pp.1-27, 2021. – reference: 25) Y. Yeh, C. Huang, and Y. Wang: Heterogeneous domain adaptation and classification by exploiting the correlation subspace, IEEE Transactions on Image Processing, Vol.23, No.5, pp.2009-2018, 2014. – reference: 27) N. Ogawa, K. Maeda, T. Ogawa, and M. Haseyama: Correlation-aware attention branch network using multi-modal data for deterioration level estimation of infrastructures, Proc. International Conference on Image Processing, pp.1014-1018, 2021. – reference: 24) K. Maeda, S. Takahashi, T. Ogawa, and M. Haseyama: Distress classification of road structures via adaptive bayesian network model selection, Journal of Computing in Civil Engineering, Vol.31, No.5, pp.04017044_1-04017044_13, 2017. – reference: 2) American Water Works Association: State of the water industry, 2021, from https://www.awwa.org/Portals/0/Awwa/Professional%20Development/SOTWI_2021_Full_Report.pdf, (accessed 2022-06-28). – reference: 30) N. Ogawa, K. Maeda, T. Ogawa, and M. Haseyama: Deterioration level estimation based on convolutional neural network using confidence-aware attention mechanism for infrastructure inspection, Sensors, Vol.22, No.1, p.382, 2022. – reference: 6) S. Li and M. Pozzi: What makes long-term monitoring convenient? a parametric analysis of value of information in infrastructure maintenance, Structural Control and Health Monitoring, Vol.26, No.5, p.e2329, 2019. – reference: 7) L. Yang, B. Li, W. Li, Z. Liu, G. Yang, and J. Xiao: Deep concrete inspection using unmanned aerial vehicle towards cssc database, iProc. International Conference on Intelligent Robots and Systems, pp.24-28, 2017. – reference: 16) J. Hu, L. Shen, and G. Sun: Squeeze-and-excitation networks, Proc. International Conference on Computer Vision and Pattern Recognition, pp.7132-7141, 2018. – reference: 20) K.H. Lee, C. Park, J. Oh, and N. Kwak: Lfi-cam: Learning feature importance for better visual explanation, Proc. International Conference on Computer Vision, pp.1355-1363, 2021. – reference: 26) K. Maeda, S. Takahashi, T. Ogawa, and M. Haseyama: Estimation of deterioration levels of transmission towers via deep learning maximizing canonical correlation between heterogeneous features, IEEE Journal of Selected Topics in Signal Processing, Vol.12, No.4, pp.633-644, 2018. – reference: 23) Y. Gao and K.M. Mosalam: Deep transfer learning for image-based structural damage recognition, Computer-Aided Civil and Infrastructure Engineering, Vol.33, No.9, pp.748-768, 2018. – reference: 28) B. Settles: Active learning literature survey, University of Wisconsin-Madison Department of Computer Sciences, pp.1-65, 2009. – reference: 29) J. Moon, J. Kim, Y. Shin, and S. Hwang: Confidenceaware learning for deep neural networks, Proc. International Conference on Machine Learning, pp.7034-7044 2020. – reference: 12) A. Krizhevsky, I. Sutskever, and G.E. Hinton: Imagenet classification with deep convolutional neural networks, Proc. Advances in neural information processing systems, pp.1097-1105, 2012. – reference: 13) M. Everingham, S. Eslami, L. Van Gool, C.K. Williams, J. Winn, and A. Zisserman: The pascal visual object classes challenge: A retrospective, International journal of computer vision, Vol.111, No.1, pp.98-136, 2015. – reference: 11) B. Patterson, G. Leone, M. Pantoja, and A.A. Behrouzi: Deep learning for automated image classification of seismic damage to built infrastructure, Proc. 11th US National Conference on Earthquake Engineering, pp.1-11, 2018. – reference: 5) Andrea D’Ariano，L. Meng，G. Centulio，F. Corman: Integrated stochastic optimization approaches for tactical scheduling of trains and railway infrastructure maintenance, Computers & Industrial Engineering, Vol.127， pp.1315-1335, 2019. |
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| Title | 異種特徴間の相関およびAttention Mapの確信度を考慮可能な変状画像の劣化レベル分類 |
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