Research on image enhancement algorithm for the monitoring system in coal mine hoist

Abstract

As the mine hoist monitors video images with poor light, low brightness, heavy dust, and low contrast, the monitoring video images are not conducive to monitoring. They cannot meet the needs of applications. Based on actual video surveillance data, this paper proposes a dark channel prior (DCP) method integrated with a guided image filter video image enhancement algorithm. Firstly, we analyzed the characteristics of the mine hoist system’s video images. Then, the DCP technique was used to enhance the video images. A guided image filter algorithm was introduced to ensure that the video has more clarity and visual impact. Comparing the suggested method to the other four algorithms, it performed better both subjectively and objectively than the algorithms mentioned above. Experiments demonstrate that the proposed technique can successfully improve the entire clarity and contrast of video images while avoiding the over-enhancement of bright areas close to the light source, meeting the practical application requirements of video surveillance.

A Roadheader Positioning Method Based on Multi-Sensor Fusion

“…These darkened segments often blur image contours, resulting in the loss of intricate details and subpar visual appeal. The presence of coal dust exacerbates the situation by diminishing both image contrast and the signal-to-noise ratio, consequently reducing the number of image feature points, and thereby hampering effective feature extraction [23][24][25]. To combat this challenge, it becomes imperative to deploy specific image-enhancement techniques aimed at ameliorating the overall quality of these mine-based visuals.…”