Method for recognizing coal and rock on basis of co-occurrence features of image blocks

Abstract

The invention discloses a method for recognizing coal and rock on the basis of co-occurrence features of image blocks. The method includes densely extracting the image blocks, extracting key image blocks by the aid of clustering algorithms, labeling coal and rock images by the aid of vectors of the key image blocks, computing co-occurrence matrixes of the labeled images, and extracting energy, contrast and inverse difference moment and entropy of the co-occurrence matrixes to form features of the images; comparing the to-be-recognized images to various modes and determining that the most similar modes are categories of the to-be-recognized images. The features of each sample image represent the corresponding mode of the coal or the rock. The method has the advantages of little influence of illumination and imaging view point change, high recognition rate and good stability.

Description

technical field [0001] The invention relates to a method for identifying coal rocks by using image block co-occurrence features, belonging to the field of coal rock identification. Background technique [0002] Coal and rock identification is to use a method to automatically identify coal and rock objects as coal or rock. In the process of coal production, coal rock identification technology can be widely used in the production links such as drum coal mining, tunneling, caving coal mining, and raw coal gangue selection. The safe and efficient production of coal mines is of great significance. [0003] There are many coal rock identification methods, such as natural γ-ray detection method, radar detection method, stress pick method, infrared detection method, active power monitoring method, vibration detection method, sound detection method, dust detection method, memory cutting method etc., but these methods have the following problems: ① It is necessary to install various...

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Problems solved by technology

[0003] There are many coal rock identification methods, such as natural γ-ray detection method, radar detection method, stress pick method, infrared detection method, active power monitoring method, vibration detection method, sound detection method, dust detection method, memory cutting method etc., but these methods have the following problems: ① It is necessary to install various sensors on the existing equipment to obtain information, resulting in complex structure and high cost of the device

② Shearer drums, roadheaders and other equipment are subjected to complex forces, severe vibrations, severe wear, and large dust during the production process. It is difficult to deploy sensors, which easily leads to damage to mechanical components, sensors, and electrical circuits, and poor device reliability.

③ For different types of mechanical equipment, there is a big difference in the optimal type of sensor and the selection of signal pickup points, which requires personalized customization and poor adaptability of the system

[0004] There are existing methods to identify coal rocks using the texture features of coal rock images, such as the coal rock recognition method based on gray level co-occurrence statistical features, the image gray level is not robust to changes in illuminance and viewpoint, and coal, In the workplaces of rock recognition, such as working face and excavation face, the illumination changes are often very common, and the viewpoint of the imaging sensor also changes in a large range, so the recognition is unstable and the recognition rate is not high

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