MFPA-Net: An efficient deep learning network for automatic ground fissures extraction in UAV images of the coal mining area - Xiao Jiang; Shanjun Mao; Mei Li; Hui Liu; Haoyuan Zhang; Shuwei Fang; Mingze Yuan; Chi Zhang (International Journal of Applied Earth Observations and Geoinformation, 2022)
Ground fissures caused by high-intensity underground coal mining activities will damage the ecological environment and endanger mine safety. The complicated surface conditions in coal mining areas make fissure detection labor-intensive work, and manual fissure extraction is inefficient, restricting the surface monitoring work. In this study, an efficient Deep Learning (DL) network named MFPA-Net (Multi-scale Feature Pyramids and Attention Network) is proposed to extract ground fissures in Unmanned Aerial Vehicle (UAV) images from the coal mining area automatically and accurately. In MFPA-Net, the Dilated Residual Networks (DRN) are used to extract diverse context information, the Dual Attention Mechanism (DAM) is introduced to integrate the dependence of pixels' spatial location and feature channels to generate high-level features, the Atrous Spatial Pyramid Pooling (ASPP) is utilized for mining multi-scale context information from high-level features, and the Multi-scale Feature Pyramid Network (MFPN) is designed to combine high-level and low-level features. Moreover, the Focal Tversky Loss Function is adopted to handle the unbalanced samples. To promote DL technologies application in the fissure monitoring of mining areas, the GFCMA (Ground Fissures of the Coal Mining Area) dataset is constructed. Experiments on GFCMA show that MFPA-Net can achieve high Precision (69.4%), Recall (70.7%), F1-Score (70.0%), and Mean Intersection over Union (MIoU) (75.1%) simultaneously, which significantly outperform traditional image processing methods and recently DL networks. Experiments on public pavement datasets Crack500 and DeepCrack prove MFPA-Net's high reliability and widespread applicability. The performance of the trained MFPA-Net on real large-scale scenarios demonstrates its practical value, strong robustness, and high efficiency. This study provides a solution for rapid monitoring of ground fissures under complicated surface conditions, which can serve the safe production and ecological restoration high-efficiently in mining areas.