Coal-rock image recognition method for mining and heading face based on spatial pyramid pooling structure

Coal androck identification is one of the key technologies to realize intelligent and unmanned mining and heading. In order to further improve the accuracy and efficiency of coal rock image recognition technology, a coal-rock image segmentation model(Coal-Rock Pyramid Network, CRPN) based on spatial pyramid pooling architecture and convolutional neural network is proposed in this paper. ① Depthwise separable convolution is used in the coding part of CRPN. With the support of hybrid dilated convolution and residual convolution embedded with the global attention mechanism, the computational efficiency and the receptive field have been improved, and the adverse effects of global irrelevant features on subsequent feature maps are reduced. The computing framework based on the spatial pooling architecture is applied to the decoding part, weakening the loss of related information between different regions in the feature map, and enhancing the effective representation of global information. ② To ensure the effectiveness of model training, The in-situ coal and rock images from the thin seam mining face are collected by high sensitivity underground explosion-proof SLR camera, including four typical conditions:complete state, crack and shadow, dark light, dark light with wire mesh supporting. The image preprocessing was executed, including noise addition and changing the characteristics and morphology of the image. A high-definition coal-rock image database containing 6 400 valid samples has been established. ③ A training optimization algorithm based on cross-entropy loss function and rectified adaptive moment estimation is proposed, taking into account the efficiency and accuracy. ④ The pixel accuracy and intersection over union are selected as indicators to evaluate the recognition effect of CRPN. The results show that the average values of the two indicators of CRPN are 96.05% and 91.54%, respectively, which are better than other existing coal-rock recognition models such as U-net and Segnet. The average calculation time of a single image of CRPN model is 0. 037 s, which is higher than the frame rate of mining explosion-proof camera equipment(25 fps) and proves its application deployment ability. The CRPN model was deployed in the dynamic video obtained on the working face for testing. The test results show that the model has achieved correct coal-rock recognition results under both stable and jitter conditions, verifying its feasibility and robustness in complex environments. © 2021, Editorial Office of Journal of China Coal Society. All right reserved.