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Research on Intelligent Identification of Joint Cracks in Rock Mass based on Mask R-CNN Algorithm
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Xiaobing WANG1, Junqing WANG1, lin LIU1, Shilei HU1, lei WEN2
Northwestern Geology | 2026, 59(2) : 139 - 152
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Northwestern Geology | 2026, 59(2): 139-152
Research on Intelligent Identification of Joint Cracks in Rock Mass based on Mask R-CNN Algorithm
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Xiaobing WANG1, Junqing WANG1, lin LIU1, Shilei HU1, lei WEN2
Affiliations
  • 1Chinga Nuclear Industry Survey Design & Research Co., Ltd., Zhengzhou 450000, Henan, China
  • 2Department of Engineering Mechanics, Shijiazhuang Tiedao University, Shijiazhang 050043, Hebei, China
Published: 2026-04-20 doi: 10.12401/j.nwg.2025028
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The identification and measurement of rock joint cracks is an important basis for the characteristics of rock structural planes and the evaluation of rock quality. The traditional identification of rock joint cracks in boreholes is mainly manual and inefficient. In this paper, the deep learning convolution neural network Mask R-CNN algorithm model is used to construct the data set by labeling and standardizing the features of the image joints and cracks. The Mask R-CNN model is trained to construct the intelligent recognition and segmentation model of rock joints and cracks, so as to realize the intelligent recognition and segmentation of the observed image. The rock joint cracks are skeletonized by binarization and sine wave fitting, and the occurrence of rock joint cracks is calculated, so as to realize the statistics of rock joint cracks on the borehole wall by digital means. The technology is compared and verified by project examples. The results show: ①the non-directional rock joint fracture can be extracted by a specific algorithm model, and its corresponding occurrence can be calculated by multi-point fitting of sine curve. ②The accuracy of the measurement results can reach more than 85 %. ③With the increase of the number of different morphological samples of joint cracks, the recognition accuracy can be increased. ④The intelligent identification and measurement results of rock joint cracks can describe the rock structural plane in detail and be applied to the evaluation of rock integrity and quality grade.

rock joint cracks  /  feature proposition  /  skeleton segmentation  /  curve fitting  /  cracks occurrence  /  identification, measurement
王晓兵, 王俊卿, 刘琳, 胡石磊, 闻磊. 基于Mask R-CNN算法的岩体节理裂隙智能化识别研究. 西北地质, 2026 , 59 (2) : 139 -152 . DOI: 10.12401/j.nwg.2025028
Xiaobing WANG, Junqing WANG, lin LIU, Shilei HU, lei WEN. Research on Intelligent Identification of Joint Cracks in Rock Mass based on Mask R-CNN Algorithm[J]. Northwestern Geology, 2026 , 59 (2) : 139 -152 . DOI: 10.12401/j.nwg.2025028
Year 2026 volume 59 Issue 2
172
80
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Article Info
doi: 10.12401/j.nwg.2025028
  • Receive Date:2023-10-19
  • Online Date:2026-09-16
  • Published:2026-04-20
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History
  • Received:2023-10-19
  • Revised:2025-02-11
  • Accepted:2025-03-10
Affiliations
    1Chinga Nuclear Industry Survey Design & Research Co., Ltd., Zhengzhou 450000, Henan, China
    2Department of Engineering Mechanics, Shijiazhuang Tiedao University, Shijiazhang 050043, Hebei, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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