An Underground Energized Cable Detection Method Based on the Fusion of Real-World Simulation and Characteristic Time Sequence

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.