Safety monitoring method of moving target in underground coal mine based on computer vision processing

Abstract

Coal is one of the main energy sources in China. The country attaches great importance to the development of coal mining industry, and coal production is on the rise. At the same time, mine safety accidents are becoming more and more frequent, and the country is paying more and more attention to mine safety accidents. The underground environment of coal mine is complex, noisy and uneven, and there will be problems such as occlusion and high false detection rate during video monitoring. In order to ensure the safety of underground personnel, moving target detection and tracking based on video monitoring information is of great significance for coal mine safety production. The purpose of this paper is to study how to analyze and study the monitoring of moving targets in coal mines based on computer vision processing, and describe the image processing methods. This paper puts forward the problem of target monitoring, which is based on image processing, and then elaborates on the concept of image enhancement and related algorithms. From the average gradient, the algorithm in this paper is 56.60% higher than the histogram equalization algorithm, and 68.26% higher than the dark primary color prior dehazing algorithm. and designs and analyzes cases of image enhancement in coal mines. The experimental results show that the information entropy of the algorithm in this paper is 31.10% higher than that of the dark primary color prior dehazing algorithm, and 18.72% higher than that of the histogram equalization algorithm. It can be seen that the algorithm in this paper can achieve better enhancement effect.

UM‐GAN: Underground mine GAN for underground mine low‐light image enhancement

“…However, in the underground mine environment, factors such as insufficient illumination and light attenuation are intertwined and complicated, which seriously affecting the quality of images in dark scenes such as mine roadways, making them difficult to recognize and interpret. low‐light image enhancement technology is critical in many underground mine application scenarios, such as mine safety monitoring [4] and underground mine target recognition [5].…”

TMCD: Threshold multi-channel prior-based defogging algorithm for underground coal mine images

“…These factors lead to significant blurring and degradation of images, severely impacting their visibility and the ability to extract information 15 . Therefore, the research and application of image dehazing technology are crucial for enhancing underground monitoring and ensuring safe production in coal mines 16,17 . However, most current image dehazing algorithms are only suitable for specific types of haze or atmospheric interference, and their effectiveness in the complex and variable conditions of underground coal mines is not guaranteed 18 .…”

Research on Synchronous Control of Active Disturbance Rejection Position of Multiple Hydraulic Cylinders of Digging-Anchor-Support Robot

“…At the same time, safety accidents have occurred frequently in our country's coal mines, which is the issue that focuses on the safety field. The problem of coal mine safety is not being solved effectively, which is one of the key reasons limiting the rapid development of the coal industry [1][2][3]. Among the numerous coal mine safety accidents that have occurred, coal mine safety accidents caused by roof problems account for the highest proportion of total safety accidents [4].…”