An Underground Energized Cable Detection Method Based on the Fusion of Real-World Simulation and Characteristic Time Sequence
- 1Jiangmen Power Supply Bureau, Guangdong Power Grid Co., Ltd., Jiangmen, China
- 2School of Mechanical & Electrical Engineering, Wuhan Institute of Technology, Wuhan, China
Abstract
Detecting live underground power cables in complex subsurface environments with ground-penetrating radar (GPR) is hindered by scarce labels, simulation–reality gaps, and weak deep echoes. We present ground-penetrating physics-aware modeling YOLO (GPM-YOLO), a detection framework coupling realistic simulation with physics-aware and depth-sequence fusion. First, we build a realistic GPR dataset by modeling non-uniform layered soils, plastic/concrete conduits, and stones, and by injecting power-frequency excitation into the cable core to emulate “live-cable” signatures, yielding systematic B-scans. Second, a physics feature module separates and adaptively fuses two cues: high-frequency strong reflections from the metal core and low-frequency oscillatory textures from the insulation. Third, a depth-sequence fusion module treats the B-scan vertical axis as an intrinsic temporal sequence and applies interval modeling with attention to enhance the continuity of deep weak signals and suppress false alarms. With db4 wavelet denoising, normalization, and a three-level annotation protocol (boxes, physics masks, depth evolution), GPM-YOLO achieves higher recall for deep cables and stronger robustness than vanilla YOLO on our dataset. The method unifies physical interpretability and data-driven learning for fast, accurate live-cable detection in urban settings.
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BibTeX
@article{tiancheng2026underground,
title = {{An Underground Energized Cable Detection Method Based on the Fusion of Real-World Simulation and Characteristic Time Sequence}},
author = {Sun Tiancheng and Tan Yongqiang and Yang Xi and Zhang Xitian and Ma Chengzhi and Zhou Hongjian},
journal = {Measurement Science Review},
year = {2026},
month = oct,
doi = {10.2478/msr-2026-0035},
url = {https://doi.org/10.2478/msr-2026-0035},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - An Underground Energized Cable Detection Method Based on the Fusion of Real-World Simulation and Characteristic Time Sequence
AU - Sun Tiancheng
AU - Tan Yongqiang
AU - Yang Xi
AU - Zhang Xitian
AU - Ma Chengzhi
AU - Zhou Hongjian
PY - 2026
DA - 2026/10/26
JO - Measurement Science Review
DO - 10.2478/msr-2026-0035
UR - https://doi.org/10.2478/msr-2026-0035
AB - Detecting live underground power cables in complex subsurface environments with ground-penetrating radar (GPR) is hindered by scarce labels, simulation–reality gaps, and weak deep echoes. We present ground-penetrating physics-aware modeling YOLO (GPM-YOLO), a detection framework coupling realistic simulation with physics-aware and depth-sequence fusion. First, we build a realistic GPR dataset by modeling non-uniform layered soils, plastic/concrete conduits, and stones, and by injecting power-frequency excitation into the cable core to emulate “live-cable” signatures, yielding systematic B-scans. Second, a physics feature module separates and adaptively fuses two cues: high-frequency strong reflections from the metal core and low-frequency oscillatory textures from the insulation. Third, a depth-sequence fusion module treats the B-scan vertical axis as an intrinsic temporal sequence and applies interval modeling with attention to enhance the continuity of deep weak signals and suppress false alarms. With db4 wavelet denoising, normalization, and a three-level annotation protocol (boxes, physics masks, depth evolution), GPM-YOLO achieves higher recall for deep cables and stronger robustness than vanilla YOLO on our dataset. The method unifies physical interpretability and data-driven learning for fast, accurate live-cable detection in urban settings.
SN - 1335-8871
ER -