Identification of Ground Fissures in Mining Areas from UAV Images Based on RDC-UNet

Abstract

As coal mining deepens, ground fissures in mining areas pose significant risks to safety and the environment. Traditional geological exploration methods are inefficient and costly, making the precise detection of large-scale fissures difficult. Deep learning methods using unmanned aerial vehicle (UAV) images have become a popular approach for fissure detection, although challenges such as insufficient accuracy and poor adaptability in complex backgrounds remain. This study proposes an residual-depthwise separable convolution UNet (RDC-UNet) model to address these issues, building on U-Net by incorporating residual connections (RC), depthwise separable convolutions (DSC), and the convolutional block attention module (CBAM) attention mechanism. The model was trained on a dataset of 300 UAV images from a DJI Mavic 3e and outperformed the benchmark models, achieving 86.12 % mPrecision, 75.01 % mRecall, 70.72 % mIoU, and 0.7951 mF1. Ablation experiments show that removing any core module leads to a performance drop, with the DSC module reducing mF1 by 3.28 %, CBAM decreasing mIoU by 2.67 %, and RC lowering mRecall by 3.96 %. RDC-UNet is highly efficient, requiring only 7.8 million parameters, with an inference time of 75 milliseconds and a memory footprint of 180 MB, much lower than other models. This makes it well-suited for real-time UAV-based fissure monitoring. The RDC-UNet model offers high accuracy and efficiency, making it an ideal solution for cost-effective real-time monitoring of ground fissures in mining areas.