Abstract：The morphological development of coal-rock fractures is a critical factor influencing their mechanical properties, and accurate identification of these fractures is essential for ensuring mine safety. However, existing detection methods often struggle to accurately identify fine fractures and fragmented zones in complex environments. To address this challenge, this study introduces an enhanced AttRes-UNet model based on the ResNet-UNet framework, which incorporates both channel attention (SE) and spatial attention (CBAM). The model utilizes residual structures to improve feature representation and employs attention mechanisms to enhance the extraction of key fracture regions, thereby significantly boosting fracture recognition in complex conditions. A high-quality annotated dataset of real coal-rock images was constructed, and the impacts of two residual backbone depths, ResNet18 and ResNet34, on model performance were systematically assessed. Experimental results reveal that the AttRes34-UNet model significantly outperforms traditional UNet and ResNet-UNet models in terms of accuracy, F1 score, and IoU, demonstrating superior stability and generalization in fine fracture detection and in delineating fragmented zone boundaries. The proposed AttRes-UNet model offers an efficient and precise technical approach for automatic recognition of coal-rock fractures, providing scientific support for safety monitoring and disaster prevention in coal mining operations.
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2026, Vol. 45 
