Machine-learning-based thermal infrared recognition of fractures in grotto roofs

doi:10.11835/j.issn.1000.582X.2024.10.015

Home > Archive>Volume 47, Issue 10, 2024 >191-204. DOI:10.11835/j.issn.1000.582X.2024.10.015

Machine-learning-based thermal infrared recognition of fractures in grotto roofs
DOI:
                        10.11835/j.issn.1000.582X.2024.10.015
                    
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                        LI Changbo1LI Changbo
1aSchool of Highway, Chang’an University, Xi’an710064, P. R. China
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BAO Han1,2BAO Han
1aSchool of Highway, Chang’an University, Xi’an710064, P. R. China;1bXi’an Key Laboratory of Geotechnical Engineering for Green and Intelligent Transport, Chang’an University, Xi’an710064, P. R. China
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LAN Hengxing3LAN Hengxing
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing100101, P. R. China
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LI Li4LI Li
China Academy of Cultural Heritage, Beijing100029, P. R. China
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CHEN Weichang4CHEN Weichang
China Academy of Cultural Heritage, Beijing100029, P. R. China
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LIU Changqing1LIU Changqing
1aSchool of Highway, Chang’an University, Xi’an710064, P. R. China
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LYU Hongtao1LYU Hongtao
1aSchool of Highway, Chang’an University, Xi’an710064, P. R. China
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Affiliation:1.1aSchool of Highway, Chang’an University, Xi’an710064, P. R. China;2.1bXi’an Key Laboratory of Geotechnical Engineering for Green and Intelligent Transport, Chang’an University, Xi’an710064, P. R. China;3.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing100101, P. R. China;4.China Academy of Cultural Heritage, Beijing100029, P. R. China
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Fund Project:Supported by National Natural Science Foundation of China(42177142, 42041006), and the Fundamental Research Funds for the Central Universities(300102212213).

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Abstract:

The cracks developed in the layered rock mass of grotto roofs intersect with each other, which can easily cause instability and failure of the cave rock mass. Rapid and precise fracture identification is crucial for grotto protection. To meet the need for non-contact, precise fracture measurement, this study integrates thermal infrared detection technology with an improved UNet network model to extract binary maps of roof fracture networks. Clustering algorithms are employed for segmentation and recognition, achieving a Dice coefficients of 71.63% and a detection speed of 0.84 frames/s. The method exhibits high extraction efficiency, accuracy, good applicability of thermal infrared images and resilience against artificial structure influence. Applied to the roof of Anyue Yuanjue Grotto, this method successfully identified 153 fractures and reveals dominant fracture trends at NW327° and NE55°, outperforming other measurement techniques.

Key words:grotto temple;rock mass fracture identification;deep learning;UNet network;partitioning of fracture;cluster analysis

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李昌波,包含,兰恒星,李黎,陈卫昌,刘长青,吕洪涛.基于机器学习的石窟顶板裂隙热红外识别[J].重庆大学学报,2024,47(10):191~204

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Received:January 10,2024
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Online: November 14,2024
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