A mine belt anomaly detection method and device based on video processing

Abstract

The invention relates to a mine belt anomaly detection method and device based on video processing. A high-definition camera is installed at a coal drop port between the belt conveyors. The camera shoots a picture directly opposite the coal drop port, and the video information of the coal drop port is collected in real time, and the processing process is visualized. The method comprises the following steps of: preprocessing a frame image extracted from a monitoring video of a coal nozzle, wherein the preprocessing comprises image grayscale transformation and filtering; Calculating the ratio ofthe number of pixels nc of coal particles in the preprocessed image to the total number of pixels n of the delimited detection area to obtain the relative area lambda s; When the relative area lambdas is greater than the first threshold value Ts1, determining that there is a large coal block or a coal piling condition in the image; the first threshold value Ts1 has a range of 0<Ts1<1. The invention can intelligently detect the possible large coal lump or coal piling at the coal nozzle, and the method can enhance the detection robustness, reduce the calculation amount, reduce the calculationerror, and speed up the operation.

Description

technical field [0001] The invention relates to the technical field of intelligent detection of mine environment, in particular to a method and device for abnormal detection of mine belts based on video processing. Background technique [0002] With the continuous development of my country's coal industry, underground coal transportation tends to be high-speed and belt-based. Belt conveyors have become the main tool for coal transportation in various coal mines. In the process of coal transportation, there may be abnormal situations such as large coal piles, anchor rods inserted and torn belts, etc., causing damage to equipment and burying hidden dangers for underground safety. At present, the methods for abnormal detection of belt conveyors are mainly manual detection and sensor detection. However, manual detection is time-consuming and labor-intensive. One person has to monitor multiple belt conveyors at the same time. When an abnormality occurs, it is often difficult to...

AI Technical Summary

Problems solved by technology

[0006] 2. Existing methods have complex algorithms in the detection and extraction of moving coal blocks, and the detection results lack robustness

[0007] 3. In terms of coal block area calculation, the existing algorithms are mainly to obtain the coal block outline and calculate the area of ​​the connected area, but it is usually difficult to capture the connected area of ​​the coal block during the coal falling process, and the calculation results have large errors

[0008] 4. Existing algorithms cannot detect bolts that may appear during the coal falling process

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