Coal-rock identification method based on image gray level co-occurrence matrixes

Abstract

The invention discloses a coal-rock identification method based on image gray level co-occurrence matrixes. The coal-rock identification method comprises the following steps of: respectively collecting color images f1 and f2 of a known coal sample and a known rock sample under the same imaging condition; respectively capturing sub-images s1 and s2 which are the same in size and free of a background; extracting gray level co-occurrence matrixes P1 and P2 of the sub-images; calculating image characteristics K1 and K2 based on the gray level co-occurrence matrixes; for an unknown coal-rock object to be identified, collecting an image fx under the same imaging condition; capturing a sub-image sx free of the background according to the same size; extracting a gray level co-occurrence matrix PXof the sx; calculating an image characteristic value Kx based on the gray level co-occurrence matrixes; and judging the coal-rock type according to a relation between Kx and K1 and K2. Because non-contact identification based on the images is utilized, the coal-rock identification method has the characteristics of easy deployment, strong adaptability, high identification rate and the like.

Description

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AI Technical Summary

Benefits of technology

This technology uses an imageous method called images from cameras or sensors placed near each other without physical contact between them. It allows accurate measurements with good accuracy even at very small distances around it's own body. These techniques are easily deployed by operators who want to quickly detect different types of objects such as rocks and coals during manufacturing processes.

Problems solved by technology

This technical problem addressed in this patents relates to improving the accuracy and safety of coal-rower excavators (CWS) while minimizing costs associated with these activities. Current systems require manual input from operators who often overlook important factors like the shape of the ground being worked upon instead of actually understanding them correctly. Therefore, new techniques aim to automate the measurement task without human intervention would improve productivity and reduce risk of accidents caused by unstable surfaces underground.

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