Coal rock recognition method based on relativity measurement learning

Abstract

The invention discloses a coal rock recognition method based on relativity measurement learning. According to the method, a new relativity measurement function is learnt from a training sample set in a monitoring mode to measure the relativity of coal and rock image samples, so that the relativity measurement value of samples in the same type is larger and larger, the relativity measurement value between samples in different types is smaller and smaller, and the classification rate of unknown samples is increased. The method includes an image preprocessing process, a training process and a recognition process. A preprocessing module is used for simple preprocessing of collected coal and rock images to obtain the training sample set. A training module is used for learning the optimal coal rock classification effect relativity measurement function from the training sample set. A recognition module is used for performing measurement classification by means of the optimal relativity measurement function. By means of the method, images of coal and rock under different illuminances and different viewpoints serve as the training samples, the method is little influenced by illuminance and imaging viewpoint changes, the recognition rate is high, and stability is good.

Description

technical field [0001] The invention relates to a coal rock identification method based on correlation measurement learning, which belongs to the technical 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 insta...

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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

In coal production, where coal and rock identification are required, such as working faces and tunneling faces, illumination changes are often common, and the viewpoint of the imaging sensor also changes in a large range, while the two-dimensional texture model does not have the ability to control changes in illumination and viewpoint. Robustness, so the recognition is unstable and the recognition rate is not high

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