# Research on Coal and Rock Recognition in Coal Mining Based on Artificial Neural Network Models
###### Abstract
Coal gangue is close to coal in color and is a black-gray rock with a greater hardness than coal. However, the image recognition technology based on visual features proposed in this paper can still distinguish the two sheets. The neural network model designed in this paper relies on extracting and comparing the gray features and texture features of the target to identify coal and rocks. Although the color of coal gangue is close to that of coal, there are great differences in texture features. Compared with gangue, the surface of coal is more glossy and easier to reflect, while the surface of gangue has a more diffuse reflection of light and thus almost no reflection. In this paper, in the study of coal and rock identification technology in a fully mechanized coal mining face, coal gangue is classified as a kind of rock, and coal is distinguished. More and more experts and scholars use image recognition technology to solve the problem of coal and rock recognition in coal mine production. [PERSON] et al. proposed a method of a convolutional neural network to identify the coal-rock interface. Aiming at the problem of over-fitting caused by few training samples, three regularization methods, a dropout, L2 regularization, and batch normalization, were used to construct the coal-rock image data set, and the coal-rock image data set was enhanced by adding noise, image scaling, and image rotation. The experimental results show that the designed network has a better coal-rock image recognition performance [8]. A new coal-rock image segmentation algorithm model, the Coal-Rock Pyramid Network, was proposed by [PERSON] et al., which was trained by collecting three types of images of coal-rock integrity, crack shadows, and dark light. Compared with U-net and Segnet image segmentation algorithms, the recognition accuracy of this algorithm was 2.46% and 7.64% higher than that of the U-net algorithm and Segnet algorithm, respectively, reaching 91.54% [9]. [PERSON] et al. used deep separable convolution to improve the YOLOv3 algorithm. In view of the uncountable and continuous characteristics of the coal-rock interface, the ratio of the total projection length of the prediction frame in the x and y directions to the total projection length of the coal-rock interface curve is used as the evaluation standard of target detection accuracy. The results show that the recognition accuracy of the improved YOLOv3 model in the x direction is 5.85% higher than that of the original model, which is 89.89%. The recognition accuracy in the y direction is 16.99% higher than that of the original model, which is 73.30% [10]. The coal and rock recognition technology for coal mining areas mainly includes two aspects. (1) A judgment of the cutting target of the shearer: There are three situations in the mining area when the shearer works [11]. First, it is necessary to maintain the current operating power to cut coal when encountering coal seams. The second is to stop the operation of the shearer when it encounters the rock layer and to start cutting coal again after selecting the appropriate way to complete the rock breaking. Thirdly, when the coal-rock mixed layer is encountered, that is, the mining area contains both a coal seam and rock stratum, it is necessary to adjust the cutting height of the shearer drum to ensure that the drum can cut coal along the boundary between the coal seam and rock stratum, so as to avoid encountering rocks in the cutting process. (2) The determination of the distribution state of the coal seam and rock stratum in coal-rock mixed layers: the distribution state of a coal seam and rock stratum in a coal-rock mixed interface is accurately identified by coal and rock recognition technology, so that the shearer can cut coal along the boundary of the coal-rock mixed layer [12].