Method and system for coal and rock boundary dividing based on coal and rock image feature extraction and classification and recognition

Abstract

Disclosed are a method and a system for coal and rock boundary dividing based on coal and rock image feature extraction and classification and recognition. The method includes firstly, photographing coal and rock images on a coal mining working face, extracting texture feature information of the coal and rock images, and constructing a feature vector, wherein the texture feature information comprises the original images and the angular second moment, the contrast, the correlation, the mean value and the variance of every low-frequency coefficient image with Daubechies wavelet decomposition scale, and the texture feature information also comprises the total mean value and the total variance for calculating high-frequency coefficient images in horizontal, vertical and diagonal directions; and lastly, creating a best classifier to recognize the coal and rock boundary. The system comprises an image acquisition module, a feature module, a classification and recognition module, a result display interactive module, a memory module and a central control module. The method and the system for the coal and rock boundary dividing based on the coal and rock image feature extraction and the classification and recognition have simple calculation, less human intervention and low cost, can improve classification accuracy and efficiency of the coal and rock images in complex environments effectively, and provides accurate and reliable coal and rock boundary information.

Description

technical field [0001] The invention belongs to the field of computer image processing and pattern recognition. It uses image texture feature information to perform feature extraction on coal and rock images, and uses support vector machines, neural networks, etc. to classify feature vectors so as to identify the coal seam and roof of the coal mining face. Methods and systems for bedrock formation interfacing. Background technique [0002] In the process of underground coal mine mining, it is necessary to accurately identify the coal seam and rock formation, so as to control the lifting of the rocker arm of the shearer, so as to avoid cutting the roof and floor rocks. But at present, the adjustment of the height of the shearer drum is mainly judged by the operator through a manual method. As we all know, the complex environment of the underground coal mining face is not conducive to the operator to make accurate judgments. Therefore, the shearer will inevitably cut the roo...

AI Technical Summary

Problems solved by technology

As we all know, the complex environment of the underground coal mining face is not conducive to the operator to make accurate judgments

Therefore, the shearer will inevitably cut the roof and floor rocks, causing a series of problems: aggravating the wear of the pick and shortening the life of the pick; cause the quality of raw coal to decline

This method requires that the roof rock must contain radioactive elements, so it cannot be applied to sandstone roofs with less radioactive elements, which limits its large-scale promotion in my country

[0004] Although the coal-rock interface identification method disclosed in Chinese patent CN101922290 can automatically identify coal and rock, there are the following problems: first, high-pressure water jets will cause difficulties in drainage of coal mining sites; It is difficult to accurately perceive the different forces formed by the nozzle. Third, the equipment for collecting signals is complicated, which makes identification difficult

This method mainly uses the characteristic parameters of the gray-level co-occurrence matrix of the image to construct the feature vector, but it cannot reflect the difference in image texture from multiple scales and directions, so the constructed feature vector cannot fully reflect the surface characteristics of coal and rock. , affecting the recognition accuracy of coal-rock interface

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