Coal gangue separation method based on deep vision

Abstract

The invention discloses a coal gangue separation method based on deep vision, and relates to the technical field of robot vision. The method comprises the steps of taking ore color images of an ore separation production line as input, utilizing an ore segmentation convolutional neural network for conducting segmentation to obtain an ore area segmentation result, conducting ore area cutting on theore color images according to the ore segmentation result to obtain a plurality of ore area slices, then utilizing an ore classification convolutional neural network to output coal briquettes or coalgangue, conducting image analysis on the ore area slices to determine ore information, and finally automatically separating the coal briquettes and the coal gangue. By means of the method, rapid detection and precise recognition can be achieved, by estimating the volume class and relative position of ore, accurate guide information is provided for automatic separation of the coal gangue, and the separation efficiency is high. The two convolutional neural networks are customized special for an ore separation scene, and the coal gangue separation method has the advantages of being small in mass,high in real-time performance, high in detection precision and the like.

Description

technical field [0001] The invention relates to the technical field of machine vision, in particular to a coal gangue separation method based on depth vision. Background technique [0002] Coal gangue is solid waste discharged during the coal mining and washing process, and its output accounts for about 15% of the raw coal output. Coal gangue separation is a necessary process in the coal mine production process. Removing gangue is also the basis for coal production of clean energy. Reducing gangue particle emissions can reduce PM2.5 unit emissions. Sorting gangue can reduce the cost of washing, improve the grade of finished coal, and improve the economic benefits of coal mining enterprises. Strengthening the research on the comprehensive management and utilization of coal gangue and improving clean coal technology are important research contents of the current coal-burning country, and are also the needs of environmental protection and development. [0003] At present, mos...

AI Technical Summary

Problems solved by technology

[0003] At present, most coal preparation plants use manual visual inspection to sort coal blocks, and the sorting speed and sorting accuracy are not ideal. Some coal preparation plants also use deep vision technology to realize the identification of coal blocks and gangue for automatic sorting. However, there is a large amount of flying coal ash in the coal preparation process, which will interfere with the machine vision detection equipment. Therefore, after a period of use, the recognition accuracy will be reduced, the reliability will not be high, or regular manual cleaning is required, and manual intervention is still required, resulting in difficulties. Complete automation, the actual sorting effect is not ideal

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