Mining Engineering Image Recognition Method Based on Simulated Annealing Algorithm

Abstract

Because the current methods used in mining engineering image feature recognition have some problems, such as poor classification accuracy, operation efficiency, and inability to recognize rotation features, in order to promote the development of mineral processing in China and improve resource recovery, simulated annealing algorithm is applied to the process of mining engineering image feature extraction in this paper. Based on the simulated annealing algorithm, this paper introduces the image recognition technology based on the simulated annealing algorithm and uses this image recognition technology to study the separation of ore and rock according to the differences between ore and waste rock in morphology and R, G, and B primary color components. At the same time, based on the local binary mode theory, the local variance of pixels is calculated successively to obtain the variance diagram of mining engineering image. At the same time, the simulated annealing algorithm is used to calculate the vector in each direction in the variance diagram of mining engineering image, and then, the vector is combined as the image eigenvalue. The obtained eigenvalue is combined with the binary pattern feature to realize mining recognition method and feature recognition. Finally, the experimental research shows that the algorithm proposed in this paper can quickly extract the spatial data information of mining engineering image variance and reuse the information of image local binary pattern. Compared with the traditional mining engineering image feature extraction algorithm, the recognition accuracy of this algorithm can reach 85%.