Intelligent identification of coal fractures using an improved u-shaped network

Abstract

To address the challenges of coal fracture image recognition, including interference from gangue and multiscale fractures, a multiscale coal fracture segmentation network model to significantly enhance the recognition of coal fracture structures is proposed. The model significantly enhances the recognition of fracture structures based on a U-shaped network architecture and the incorporation of several advanced techniques, including transfer learning, depthwise separable atrous convolutions, and residual modules. Transfer learning, by leveraging pretrained visual geometry group 16-layer network weights, bolsters the feature extraction capabilities of an encoder. Simultaneously, the integration of depthwise separable atrous convolutions and residual modules optimizes a decoder, thereby improving segmentation accuracy and the robust recognition of fractures within images. Experimental results based on qualitative and quantitative data showed that the proposed model surpassed traditional convolutional neural networks, demonstrating proficiency in identifying multiscale fractures in complex coal images. The model was applied to the identification of fractures in roadway surrounding rock boreholes. By extracting fractures from borehole imaging videos and planar diagrams, and conducting cross-validation, the study precisely delineated the fracture distribution. Additionally, to improve coal seam gas extraction efficiency, the grouting and sealing range for cross-layer extraction boreholes was determined.