土木工程、水利工程 |
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基于深度学习的公路隧道衬砌病害识别方法 |
任松( ),朱倩雯,涂歆玥,邓超,王小书 |
重庆大学 煤矿灾害动力学与控制国家重点实验室, 重庆 400044 |
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Lining disease identification of highway tunnel based on deep learning |
Song REN( ),Qian-wen ZHU,Xin-yue TU,Chao DENG,Xiao-shu WANG |
State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China |
引用本文:
任松,朱倩雯,涂歆玥,邓超,王小书. 基于深度学习的公路隧道衬砌病害识别方法[J]. 浙江大学学报(工学版), 2022, 56(1): 92-99.
Song REN,Qian-wen ZHU,Xin-yue TU,Chao DENG,Xiao-shu WANG. Lining disease identification of highway tunnel based on deep learning. Journal of ZheJiang University (Engineering Science), 2022, 56(1): 92-99.
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https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.01.010
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https://www.zjujournals.com/eng/CN/Y2022/V56/I1/92
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1 |
黄宏伟, 李庆桐 基于深度学习的盾构隧道渗漏水病害图像识别[J]. 岩石力学与工程学报, 2017, 36 (12): 2861- 2871 HUANG Hong-wei, LI Qing-tong Image recognition for water leakage in shield tunnel based on deep learning[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36 (12): 2861- 2871
|
2 |
牛晓博, 何雪婷 铁路运营隧道病害现状及检测评估[J]. 科技经济导刊, 2019, 27 (16): 86 NIU Xiao-bo, HE Xue-ting Current damage situation of railway operation tunnels and their inspection and evaluation[J]. Technology and Economic Guide, 2019, 27 (16): 86
|
3 |
林亮伦, 杜清超, 徐志武 重庆某隧道病害调查分析与治理对策[J]. 重庆建筑, 2018, 17 (8): 50- 53 LIN Liang-lun, DU Qing-chao, XU Zhi-wu Investigation analysis and treatments of tunnel diseases of a tunnel in Chongqing[J]. Chongqing Architecture, 2018, 17 (8): 50- 53
doi: 10.3969/j.issn.1671-9107.2018.08.50
|
4 |
王如路 上海轨道交通隧道结构安全性分析[J]. 地下工程与隧道, 2011, 25 (4): 37- 43 WANG Ru-lu Structural safety analysis of Shanghai rail transit tunnel[J]. Underground Engineering and Tunnel, 2011, 25 (4): 37- 43
|
5 |
刘海京, 夏才初, 朱合华, 等 隧道病害研究现状与进展[J]. 地下空间与工程学报, 2007, 3 (5): 947- 953 LIU Hai-jing, XIA Cai-chu, ZHU He-hua, et al Studies on tunnel damage[J]. Chinese Journal of Underground Space and Engineering, 2007, 3 (5): 947- 953
|
6 |
赵永贵, 刘浩, 孙宇, 等. 隧道超前预报与病害诊断技术[C]// 全国公路工程关键技术与管理信息化学术会议. 长春: 中国公路学会, 2003: 282-288. ZHAO Yong-gui, LIU Hao, SUN Yu, et al. Tunnel advanced forecast and disease diagnosis technology [C]// Tunnel Advance Forecast and Disease Diagnosis Technology National Conference on Key Technology and Management Informationization of Highway Engineering. Changchun: China Highway Society, 2003: 282-288.
|
7 |
杨正华, 张文波, 王卫东, 等 浅析地震波法用于隧道病害的诊断与预测[J]. 灾害学, 2004, 19 (1): 27- 30 YANG Zheng-hua, ZHANG Wen-bo, WANG Wei-dong, et al Analysis on tunnel hazard diagnosis and forecast by seismic wave[J]. Journal of Catastrophology, 2004, 19 (1): 27- 30
doi: 10.3969/j.issn.1000-811X.2004.01.006
|
8 |
徐宏武 渝湘高速下塘口一号隧道突水病害诊断及处治[J]. 地下空间与工程学报, 2010, 6 (4): 845- 849 XU Hong-wu Catastrophology gushing water diagnosis and treatment of Yuxiang Speedway on Xiaotangkou No. 1 Tunnel[J]. Chinese Journal of Underground Space and Engineering, 2010, 6 (4): 845- 849
|
9 |
MAKANTASIS K, PROTOPAPADAKIS E, DOULAMIS A, et al. Deep convolutional neural networks for efficient vision based tunnel inspection [C]// Proceedings of the 11th International Conference on Intelligent Computer Communication and Processing. Cluj-Napoca: IEEE, 2015: 335-342.
|
10 |
PROTOPAPADAKIS E, DOULAMIS N. Image based approaches for tunnels defects recognition via robotic inspectors [C]// Proceedings of the 11th International Symposium on Visual Computing. Las Vegas: Springer, 2015: 706-716.
|
11 |
ZHANG L, YANG F, ZHANG Y D, et al. Road crack detection using deep Convolutional neural network [C]// Proceedings of the 23rd International Conference on Image Processing. Phoenix: IEEE, 2016: 3708-3712.
|
12 |
CHA Y J, CHOI W, BUYUKOZTURK O Deep learning-based crack damage detection using convolutional neural networks[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32 (5): 361- 378
doi: 10.1111/mice.12263
|
13 |
汤一平, 胡克钢, 袁公萍 基于全景图像CNN的隧道病害自动识别方法[J]. 计算机科学, 2017, 4 (11A): 207- 211 TANG Yi-ping, HU Ke-gang, YUAN Gong-ping Automatic recognition method of tunnel disease based on convolutional neural network for panoramic images[J]. Computer Science, 2017, 4 (11A): 207- 211
doi: 10.11896/j.issn.1002-137X.2017.11A.043
|
14 |
胡利娜 一种基于深度学习的隧道衬砌病害检测技术[J]. 山西电子技术, 2019, 47 (5): 38- 40 HU Li-na Tunnel lining disease detection technology based on deep learning[J]. Shanxi Electronic Technology, 2019, 47 (5): 38- 40
doi: 10.3969/j.issn.1674-4578.2019.05.012
|
15 |
薛亚东, 李宜城 基于深度学习的盾构隧道衬砌病害识别方法[J]. 湖南大学学报:自然科学版, 2018, 45 (3): 100- 109 XUE Ya-dong, LI Yi-cheng A method of disease recognition for shield tunnel lining based on deep learning[J]. Journal of Hunan University: Natural Sciences, 2018, 45 (3): 100- 109
|
16 |
LIU W, ANGUELOV D, ERHAN D, et al. Ssd: single shot multibox detector [C]// European conference on computer vision. Cham: Springer, 2016: 21-37.
|
17 |
FU C Y, LIU W, RANGA A, et al. DSSD: deconvolutional single shot detector [EB/OL]. [2017-01-23]. https://arxiv.org/abs/1701.06659.
|
18 |
JEONG J, PARK H, KAWK N. Enhancement of SSD by concatenating feature maps for object detection [EB/OL]. [2018-05-17]. https://arxiv.org/abs/1705.09587.
|
19 |
LI Z, ZHOU F. FSSD: feature fusion single shot multibox detector [EB/OL]. [2017-05-26]. https://arxiv.org/abs/1712.00960v1.
|
20 |
卢健, 何金鑫, 李哲, 等 基于深度学习的目标检测综述[J]. 电光与控制, 2020, 27 (5): 56- 63 LU Jian, HE Jin-xin, LI Zhe, et al A survey of target detection based on deep learning[J]. Electronics Optics and Control, 2020, 27 (5): 56- 63
doi: 10.3969/j.issn.1671-637X.2020.05.012
|
21 |
IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift [EB/OL]. [2015-03-02]. https://arxiv.org/abs/1502.03167.
|
22 |
SZEGEDY C, LIU W, JIA YQ, et al. Going deeper with convolutions [C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 1-9.
|
23 |
HOWARD A G, ZHU M, CHEN B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications [EB/OL]. [2017-04-17]. https://arxiv.org/abs/1704.04861.
|
24 |
DAI Ji-feng, LI Yi, HE Kai-ming, et al. R-FCN: object detection via region-based fully convolutional networks [C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona: ACM, 2016: 379-387.
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