Research on method of acquiring high quality coal gangue images under multi-factor illumination condition

Aiming at the problem of poor imaging quality of coal and gangue surface features and low identification rate of coal and gangue caused by insufficient illumination conditions in the process of online coal separation,a new high-quality coal gangue image acquisition that integrates multiple factors such as light source distribution, color temperature, and light intensity is proposed. First of all, in view of the uneven illumination caused by the complexity of the illumination conditions in the actual washing process of coal gangue, the influence of different incident angles on the uniformity of illumination is studied based on the nine-point sampling method, and the best incident angle suitable for coal flow is determined. Then, in view of the problem of coal gangue image distortion caused by different color temperatures caused by different color reproducibility, indicators such as MSE, PSNR, and SSIM are used to quantitatively analyze the image distortion of multi-sample single type coal gangue, and the TOPSIS algorithm is used to study the effect of light sources with different color temperatures on coal and gangue. And the influence of the image distortion of the three different working conditions of mixed coal gangue to determine the color temperature with the best image quality. Finally, considering that online light intensity changes affect the expression of coal gangue surface feature information, based on the response curve of coal gangue surface characteristics under different illuminances, the correlation relationship between exposure time, belt speed and ambient light intensity is established to quantify the illuminance interval with greater surface discrimination.and determine the optimal light conditions. The coal gangue recognition system is established by fusion of high-quality image acquisition methods under multi-factor lighting conditions, and the SSD and Faster-RCNN target detection models are experimentally verified. The results show that this method has greatly improved the image quality of coal gangue, and the accuracy of coal gangue identification has been increased by 10.5%, providing more accurate data support for the intelligent coal gangue sorting system, and has certain application and promotion value for improving the raw coal selection rate.