Intelligent detection technology of dust concentration in mines based on dark channel prior

Mine dust is one of the important reasons that endanger mine personnel and equipment. Mine dust concentration detection is an important work content of coal safety management. Due to the complexity of the coal mine environment, the traditional mine dust concentration sensor has a small detection range and a high false alarm rate. The existing video dust concentration detection algorithm cannot eliminate the influence of luminosity on dust concentration detection and cannot locate the unevenly distributed dust concentration, resulting in low detection accuracy and limited detection range. In view of the above problems, an intelligent detection technology of mine dust concentration based on dark channel prior is proposed. An intelligent detection test platform for dust concentration was built to collect dust images under different luminosity. The dark channel prior algorithm was used to extract the dark channel image of the dust image, and the image transmittance was calculated. Pearson correlation coefficient method was used to calculate the correlation between image transmittance and dust concentration under different luminosity. An ambient light detection algorithm is designed to detect the ambient luminosity, and a mathematical model of dust concentration and image transmittance under different luminosity is established to calculate the dust concentration value. Aiming at the problem of uneven distribution of dust concentration in the environment, a dust area location algorithm is proposed to distinguish and process the dust concentration in different areas.In order to verify the practicability and accuracy of the algorithm in this paper, the algorithm is used to detect the images of different dust concentrations under different luminosity, and compared with the dust concentration sensor and the existing video detection algorithm. The results show that the proposed intelligent detection algorithm of dust concentration can effectively reduce the influence of environmental luminosity on the detection of dust concentration, and can effectively locate and calculate the uneven distribution of dust concentration. The ambient light detection algorithm can detect the luminosity from low to high under different dust concentrations, and the accuracy is 92.4%. The detection accuracy of dust concentration is 93.36%, and the average error rate is 6.64%. The proportion of the intersection and union ratio (IoU) greater than 0.5 between the dust concentration positioning area and the actual dust area was 66.7%. The dust image of Jinjitan Coal Mine is selected to verify the feasibility of the algorithm in this paper, which effectively improves the problem of dust area location and concentration detection. The research results provide technical support for intelligent detection of mine dust concentration image, and have important practical significance for accurate detection and early warning of mine dust concentration.