Objective and Methods  The unmanned aerial vehicle (UAV)-borne ground-penetrating radar (GPR) system, enjoying the advantages of high resolution and non-contact detection, is applicable to the detection of water accumulation in coal seams and goaves in mines. Therefore, this study proposed a rapid detection method based on a UAV-borne air-coupled GPR system to enhance the exploration efficiency of mining areas and reduce the time and risks of manual explorations. Given the limitations of the test scenarios in actual coal mines, this study investigated the water body of the Fanxiong reservoir in Ezhou City and surrounding drainage culverts for equivalent validation, aiming to assess the application potential of the proposed UAV-borne GPR system in water depth and cavity detection. Additionally, to improve the quality of signals collected using the system, singular value decomposition and constant proportional gain technique were employed to enhance the reflected signals from the water bottom and culverts.

Results and Conclusions  The experimental results indicate that the proposed UAV-borne GPR system could effectively detect the trend in the water depth in the Fanxiong reservoir (maximum depth: 6 m) and identify the reflected signals from the drainage culverts with a diameter of 1 m (distance from the culvert top to road surface: 1 m). This demonstrates the reliability and effectiveness of the system in water body and cavity detection. Compared to traditional methods, the data processing method based on the proposed UAV-borne GPR system can significantly enhance the reflected signals from the water bottom and culverts. Specifically, the entropy value and elevated root mean square (RMS) contrast of the processed B-scan signals increased by 12.7% (from 3.70 to 4.17) and 55% (from 0.11 to 0.17), respectively. This led to improved identifiability of waveform features, as well as enhanced accuracy and intuitiveness of data interpretation and analysis. As the UAV flight altitude increased from 2 m to 4 m, the intensity of signals from the water bottom was significantly attenuated (RMS contrast decreasing from 0.27 to 0.22), and the radii of curvature of the culverts’ reflection curves decreased. The results of this study will provide a theoretical basis for detecting the water accumulation areas and goaves in coal mines using a UAV-borne GPR system.