Coal-rock image recognition method integrating drilling geological information

The current deep convolutional neural network models applied to coal-rock image recognition have problems such as large volume and cumbersome calculation process. It is difficult to meet real-time detection requirements, and it has poor adaptability to complex environments such as low lighting and high dust. In order to solve the above problems, a coal-rock image recognition method integrating drilling geological information is proposed. Firstly, the improved spectral residual saliency detection (ISRSD) algorithm is used to enhance the quality of coal-rock images, effectively reducing the adverse effects of complex environments on the features of coal-rock images. Secondly, the method uses the attentional VGG (AVGG) deep convolutional neural network model. The AVGG performs pruning based on VGG, adds convolutional block attention module (CBAM), and introduces adaptive learning rate adjustment strategy to efficiently extract coal-rock image features. Finally, the Bayesian model is used to integrate the feature