Year: 2025
Type: article
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.
Source: Elektronika ir Elektrotechnika
Author: Zhu Huashan
Institution: China National Administration of Coal Geology
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $75
OpenAlex ID: W4411538207