Mining Area Ground Fissure Identification Method Based on AR-SE-ResUNet

The accurate identification of ground fissures in mining areas is crucial for geological disaster prevention and control and ecological restoration. To address the low efficiency and poor adaptability of traditional methods and the insufficient accuracy of existing deep learning models in identifying ground fissures under complex scenarios, this paper proposes an AR-SE-ResUNet model that integrates Adaptive Rectangular Convolution (ARConv) with the Squeeze-and-Excitation (SE) attention mechanism. Using Unmanned Aerial Vehicle (UAV) images from the Huipodi mining area in Shanxi Province, a sample dataset comprising 390 images with manually annotated ground fissures was constructed. The model employs Adaptive Rectangular Convolution (ARConv) to dynamically adjust convolution kernel sizes for enhanced multi-scale feature extraction, utilizes the Squeeze-and-Excitation (SE) mechanism to perform channel-wise reweighting for suppressing background interference, and combines the encoder-decoder architecture of ResUNet to achieve hierarchical feature fusion. Experiments demonstrate that the AR-SE-ResUNet model significantly outperforms comparative models in terms of IoU (87.84%), F1-score (93.52%), and Precision (88.30%), while exhibiting high robustness in detecting slender, discontinuous fissures under complex background scenarios. The model provides a novel approach for efficient identification of ground fissures in mining areas through adaptive multi-scale feature extraction and noise suppression strategies, effectively supporting geological disaster monitoring and ecological restoration practices.