| 土木工程、交通工程 |
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| 基于深度学习的隧道衬砌多病害检测算法 |
宋娟( ),贺龙喜*(),龙会平 |
| 邵阳学院 土木与建筑工程学院，湖南 邵阳 422000 |
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| Deep learning-based algorithm for multi defect detection in tunnel lining |
| Juan SONG(),Longxi HE*(),Huiping LONG |
| School of Civil and Architectural Engineering, Shaoyang College, Shaoyang 422000, China |
| 1 |
《中国公路学报》编辑部 中国交通隧道工程学术研究综述: 2022[J]. 中国公路学报, 2022, 35 (4): 1- 40
“China Journal of Highway and Transport” editorial department Review on china's traffic tunnel engineering research: 2022[J]. China Journal of Highway and Transport, 2022, 35 (4): 1- 40
doi: 10.3969/j.issn.1001-7372.2022.04.001
|
| 2 |
董飞, 房倩, 张顶立, 等 北京地铁运营隧道病害状态分析[J]. 土木工程学报, 2017, 50 (6): 104- 113
DONG Fei, FANG Qian, ZHANG Dingli, et al Analysis on defects of operational metro tunnels in Beijing[J]. China Civil Engineering Journal, 2017, 50 (6): 104- 113
|
| 3 |
HUANG H, LI Q, ZHANG D Deep learning based image recognition for crack and leakage defects of metro shield tunnel[J]. Tunnelling and Underground Space Technology, 2018, 77: 166- 176
doi: 10.1016/j.tust.2018.04.002
|
| 4 |
许力之, 王耀东, 朱力强, 等 隧道表面图像多目标智能识别算法研究[J]. 铁道学报, 2022, 44 (9): 154- 162
XU Lizhi, WANG Yaodong, ZHU Liqiang, et al Research on algorithm of intelligent recognition of mutiple objects of tunnel surface image[J]. Journal of the China Railway Society, 2022, 44 (9): 154- 162
doi: 10.3969/j.issn.1001-8360.2022.09.020
|
| 5 |
周鸣亮, 程文, 张东明, 等 运营期盾构隧道结构病害的自动化检测与三维可视化[J]. 应用基础与工程科学学报, 2021, 29 (5): 1265- 1279
ZHOU Mingliang, CHENG Wen, ZHANG Dongming, et al Automatic detection and 3D visualization of structural diseases of shield tunnel during operation[J]. Journal of Basic Science and Engineering, 2021, 29 (5): 1265- 1279
|
| 6 |
MA D, LIU J, FANG H, et al A multi-defect detection system for sewer pipelines based on StyleGAN-SDM and fusion CNN[J]. Construction and Building Materials, 2021, 312: 125385
doi: 10.1016/j.conbuildmat.2021.125385
|
| 7 |
ROSTEN E, PORTER R, DRUMMOND T Faster and better: a machine learning approach to corner detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 32 (1): 105- 119
|
| 8 |
DONG Y, WANG J, WANG Z, et al A deep-learning-based multiple defect detection method for tunnel lining damages[J]. IEEE Access, 2019, 7: 182643- 182657
doi: 10.1109/ACCESS.2019.2931074
|
| 9 |
雷明锋, 张运波, 王卫东, 等 岩石岩性Mask R-CNN智能识别方法与应用研究[J]. 铁道科学与工程学报, 2022, 19 (11): 3372- 3382
LEI Mingfeng, ZHANG Yunbo, WANG Weidong, et al Investigation and application on lithology intelligent recognition method based on Mask R-CNN[J]. Journal of Railway Science and Engineering, 2022, 19 (11): 3372- 3382
|
| 10 |
XUE Y, CAI X, SHADABFAR M, et al Deep learning-based automatic recognition of water leakage area in shield tunnel lining[J]. Tunnelling and Underground Space Technology, 2020, 104: 103524
doi: 10.1016/j.tust.2020.103524
|
| 11 |
REN Y, HUANG J, HONG Z, et al Image-based concrete crack detection in tunnels using deep fully convolutional networks[J]. Construction and Building Materials, 2020, 234: 117367
doi: 10.1016/j.conbuildmat.2019.117367
|
| 12 |
SAVINO P, TONDOLO F Automated classification of civil structure defects based on convolutional neural network[J]. Frontiers of Structural and Civil Engineering, 2021, 15 (2): 305- 317
doi: 10.1007/s11709-021-0725-9
|
| 13 |
DUNG C V Autonomous concrete crack detection using deep fully convolutional neural network[J]. Automation in Construction, 2019, 99: 52- 58
doi: 10.1016/j.autcon.2018.11.028
|
| 14 |
MANDAL V, UONG L, ADU-GYAMFI Y. Automated road crack detection using deep convolutional neural networks [C]// IEEE International Conference on Big Data . IEEE, 2018: 5212-5215.
|
| 15 |
CHENG J C P, WANG M Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques[J]. Automation in Construction, 2018, 95: 155- 171
doi: 10.1016/j.autcon.2018.08.006
|
| 16 |
周中, 张俊杰, 龚琛杰, 等 基于深度语义分割的隧道渗漏水智能识别[J]. 岩石力学与工程学报, 2022, 41 (10): 2082- 2093
ZHOU Zhong, ZHANG Junjie, GONG Chenjie, et al Automatic identification of tunnel leakage based on deep semantic segmentation[J]. Chinese Journal of Rock Mechanics and Engineering, 2022, 41 (10): 2082- 2093
|
| 17 |
彭磊, 周春, 胡锋, 等 基于深度学习的隧道掌子面节理智能检测与分割[J]. 人民长江, 2023, 54 (10): 243- 250
PENG Lei, ZHOU Chun, HU Feng, et al Research on intelligent detection and segmentation of rock joints tunnel face based on deep learning[J]. Yangtze River, 2023, 54 (10): 243- 250
|
| 18 |
刘德军, 仲飞, 黄宏伟, 等 运营隧道衬砌病害诊治的现状与发展[J]. 中国公路学报, 2021, 34 (11): 178- 199
LIU Dejun, ZHONG Fei, HUANG Hongwei, et al Present Status and development trend of diagnosis and treatment of tunnel lining diseases[J]. China Journal of Highway and Transport, 2021, 34 (11): 178- 199
doi: 10.3969/j.issn.1001-7372.2021.11.015
|
| 19 |
路耀邦, 刘永胜, 樊晓东 地铁隧道结构表观病害快速检测方法与应用[J]. 隧道建设: 中英文, 2021, 41 (Suppl.2): 655- 663
LU Yaobang, LIU Yongsheng, FAN Xiaodong Rapid detection method for surface defect for metro tunnel structure and its Application[J]. Tunnel Construction, 2021, 41 (Suppl.2): 655- 663
|
| 20 |
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 Yadong, LI Yicheng 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
|
| 21 |
LIU J, ZHAO Z, LV C, et al An image enhancement algorithm to improve road tunnel crack transfer detection[J]. Construction and Building Materials, 2022, 348: 128583
doi: 10.1016/j.conbuildmat.2022.128583
|
| 22 |
ZHOU Z, ZHANG J, GONG C Automatic detection method of tunnel lining multi-defects via an enhanced You Only Look Once network[J]. Computer-Aided Civil and Infrastructure Engineering, 2022, 37 (6): 762- 780
doi: 10.1111/mice.12836
|
| 23 |
LI Y, BAO T, LI T, et al A robust real-time method for identifying hydraulic tunnel structural defects using deep learning and computer vision[J]. Computer-Aided Civil and Infrastructure Engineering, 2023, 38 (10): 1381- 1399
|
| 24 |
王宝坤, 王如路, 陈锦剑, 等. 基于深度学习的盾构隧道表观病害自动检测方法[EB/OL]. (2023-06-13) [2024-04-21]. https://doi.org/10.16183/j.cnki.jsjtu.2023.089.
|
| 25 |
XU Y, LI D, XIE Q, et al Automatic defect detection and segmentation of tunnel surface using modified Mask R-CNN[J]. Measurement, 2021, 178: 109316
doi: 10.1016/j.measurement.2021.109316
|
| 26 |
WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway: IEEE, 2023: 7464−7475.
|
| 27 |
MAKANTASIS K, PROTOPAPADAKIS E, DOULAMIS A, et al. Deep convolutional neural networks for efficient vision based tunnel inspection [C]// 2015 IEEE International Conference on Intelligent Computer Communication and Processing . [s. l.]: IEEE, 2015: 335−342.
|
| 28 |
MEHTA S, RASTEGARI M. Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer [EB/OL]. [2024-04-21]. https://arxiv.longhoe.net/abs/2110.02178.
|
| 29 |
HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway: IEEE: 2021: 13713−13722.
|
| 30 |
CHEN J, KAO S, HE H, et al. Run, don't walk: chasing higher FLOPS for faster neural networks [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway: IEEE, 2023: 12021−12031.
|
| 31 |
MENG Z, XU S, WANG L, et al Defect object detection algorithm for electroluminescence image defects of photovoltaic modules based on deep learning[J]. Energy Science and Engineering, 2022, 10 (3): 800- 813
doi: 10.1002/ese3.1056
|
| 32 |
WANG Z, CAI Z, WU Y An improved YOLOX approach for low-light and small object detection: PPE on tunnel construction sites[J]. Journal of Computational Design and Engineering, 2023, 10 (3): 1158- 1175
doi: 10.1093/jcde/qwad042
|
| 33 |
李明超, 符家科, 张野, 等 耦合岩石图像与锤击音频的岩性分类智能识别分析方法[J]. 岩石力学与工程学报, 2020, 39 (5): 996- 1004
LI Mingchao, FU Jiake, ZHANG Ye, et al Intelligent recognition and analysis method of rock lithology classification based on coupled rock images and hammering audios[J]. Chinese Journal of Rock Mechanics and Engineering, 2020, 39 (5): 996- 1004
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