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Research on GPR image recognition of hidden diseases of tower foundation in power transmission and transformation project based on improved Faster R-CNN
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JiangZhou CHENG1, 2, JingYi YANG1, *, Gang BAO1, YingQuan LUO1
Progress in Geophysics | 2026, 41(1) : 442 - 452
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Progress in Geophysics | 2026, 41(1): 442-452
Research on GPR image recognition of hidden diseases of tower foundation in power transmission and transformation project based on improved Faster R-CNN
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JiangZhou CHENG1, 2, JingYi YANG1, *, Gang BAO1, YingQuan LUO1
Affiliations
  • 1College of Electrical and New Energy, China Three Gorges University, Yichang 443002, China
  • 2Hubei Microgrid Engineering Technology Research Center, Yichang 443002, China
Published: 2026-02-20 doi: 10.6038/pg2026II0563
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Aiming at the problem of invisible diseases of tower foundation concrete in power transmission and transformation projects caused by improper operation and related environmental factors during construction, this paper proposes a GPR image recognition method for hidden diseases of tower foundation in power transmission and transformation projects based on improved Faster R-CNN. Firstly, the traditional Faster R-CNN model is optimized, and RseNet-50 is used as the feature extraction backbone network, combined with the attention mechanism (SE module). By adding the SE module to different levels of the network and placing the SE module in different positions for comparative experiments, the optimal position in the model is judged, so that the model can reduce the redundant calculation burden on the basis of extracting the key information in the feature map. Secondly, the soft-NMS algorithm is used to improve the detection ability of closely connected targets. Finally, based on the measured data of the rigid straight column foundation, the simulation is carried out by gprMax, and the GPR image is processed by the generative adversarial network to expand the data set and enhance the learning ability of the model. The experimental results show that the average accuracy of the optimized model is 84.49%, and the F-Score is 77.58%. Compared with the traditional Faster-RCNN target detection model, the recognition accuracy is improved by 6.37% on the basis of the same data set.

Ground penetrating radar  /  Hidden disease detection  /  Faster R-CNN  /  Generative adversarial network
程江洲, 杨静怡, 鲍刚, 罗应权. 基于改进Faster R-CNN的输变电工程塔基隐性病害GPR图像识别研究. 地球物理学进展, 2026 , 41 (1) : 442 -452 . DOI: 10.6038/pg2026II0563
JiangZhou CHENG, JingYi YANG, Gang BAO, YingQuan LUO. Research on GPR image recognition of hidden diseases of tower foundation in power transmission and transformation project based on improved Faster R-CNN[J]. Progress in Geophysics, 2026 , 41 (1) : 442 -452 . DOI: 10.6038/pg2026II0563
Year 2026 volume 41 Issue 1
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doi: 10.6038/pg2026II0563
  • Receive Date:2025-04-15
  • Online Date:2026-09-23
  • Published:2026-02-20
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  • Received:2025-04-15
  • Revised:2025-10-19
Affiliations
    1College of Electrical and New Energy, China Three Gorges University, Yichang 443002, China
    2Hubei Microgrid Engineering Technology Research Center, Yichang 443002, China
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表12种不同金属材料的力学参数
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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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