Research and application of video fog concentration detection and real-time fog removal method in underground coal mine

The dynamic change of scene fog concentration caused by spraying dust removal operation in the production state of underground coal mine working face leads to the blurred visual video image. This seriously affects the visual remote intervention coal mining control and operation in underground coal mine. Aiming at the above problems, a method of video fog concentration detection and real-time fog removal underground in coal mine working face is proposed. Firstly, the difference between the brightness value and the saturation value of the fog-containing video image is calculated based on the color attenuation prior to realize the fog concentration detection. The fog-containing image and the non-fog image are further identified. Secondly, the color attenuation prior and the scene change probability model are used for correcting the video time continuous cost function. The transmittance error between adjacent frames of the video is reduced. The flicker influence of the fog removed video image is reduced. Finally, the video fog concentration detection and real-time fog removal method in the coal mine working face and the Kim method are respectively used to process the foggy video of the coal mine underground scene. The experimental results show the following points. ① The fog concentration distribution in the image scene can be accurately calculated by the fog concentration detection method. The maximum connected region of the extracted fog concentration accounts for 38.693% of the total image pixels. The fog-containing images are those greater than 20% of the fog concentration threshold. According to the recognition result of the fog-containing image, the non-fog image is automatically ignored. The fog-containing image is selectively fog removed. ② The video fog concentration detection and real-time fog removal method in coal mine working face is used to remove the fog of the production video of different areas (a support area and a coal wall area) and different fog concentrations (a medium fog concentration and a higher fog concentration). The contrast of the video image is obviously enhanced after fog removal, and the visual effect is brighter and clearer. ③ The mean square error curve of the real-time fog removal method is lower than that of Kim method. This indicates that the mean square error value of the adjacent frames of the video is reduced after the continuous scene video is fog removed. The flicker phenomenon of the real-time fog removal result video is effectively suppressed. The contrast cost function and the color information loss cost function are used to estimate the transmittance of the fog-containing image. The ideal fog removal effect is obtained when the scene changes. ④ The mean value of mean square error between the adjacent frames of the fog removal result of the real-time fog removal method is reduced by 4.26 compared with the Kim method. The similarity between the adjacent frames is improved. The image flicker phenomenon between the adjacent frames is further suppressed. In the aspect of running time, the processing time of each frame of the real-time fog removal method is increased by 2 ms compared with the Kim method. However, the processing time of each frame is less than 40 ms, which meets the real-time requirement.