Pre-perception and accurate recognition of coal–rock interface based on active excitation infrared characterization

Abstract Existing perception and identification methods for coal–rock interfaces are generally based on various cutting signals during shearer mining, but they cannot achieve pre-perception. Further, some pre-identification methods are significantly affected by the mining environment and cannot achieve accurate identification. In this study, a universal method is proposed to achieve pre-perception and accurate recognition of coal–rock interfaces. Using the coal–rock interface identification test-bed, active excitation infrared images of coal–rock were tested with different excitation time, distance, and intensities, and an image dataset was built for training the universal network model. This was done primarily to improve the universality of the model for infrared image identification of coal–rock under various parameter conditions. Second, the pyramid pooling module and MobileNetV2 were used to effectively extract the semantic features from the infrared images. Meanwhile, a convolutional block attention module was employed to improve the coal–rock interface identification ability of the proposed network. Finally, the efficiency of the proposed network model was tested on the infrared image dataset. The experimental results demonstrated that the memory occupied by the proposed network model is 9.12 MB, the test time is 38.46 ms/piece, and the intersection of union of coal and rock is 98.07 and 98.38%, respectively. Additionally, the pixel accuracy of coal and rock is 98.68 and 99.50%, respectively, which is significantly higher compared to other network models. Based on the constructed multi-parameter universality dataset, the proposed identification model of a coal–rock interface has good adaptability to active excitation infrared images acquired by different parameters and could provide the theoretical foundation and technical premise to achieve automated and intelligent mining.