Abstract: Firstly, in order to solve the problem of blurry and low definition images collected by the monitoring system in the spray dust removal scenario of coal mine working face, a image dehazing algorithm of coal mine working face based on DeDiTransformer (Density Difference-Transformer) was proposed. The algorithm uses density contrast to realize density perception, enhances the collected working face monitoring image, and improves the clarity of the personnel's safety helmet in the image. Secondly, in view of the problem that it is difficult for the coal mine working face monitoring system to quickly and accurately identify whether coal miners are wearing safety helmets, a safety helmet identification algorithm based on SAC - YOLOv9 (Supervised Atrous Convolution-YOLOv9) is proposed. The algorithm added the supervised atrous convolution into the YOLOv9 backbone extraction network to obtain receptive fields of different scales, speed up feature extraction, and improve the accuracy of safety helmet recognition. Experimental results show that the PSNR of the DeDi - Transformer algorithm on the Braize-Haze dataset is 19. 85 dB, which is 2. 49 dB higher than the DeHamer algorithm. The SSIM is 0. 7179, which is 0. 0434 higher than the DeHamer algorithm. The mAP of the SAC-YOLOv9 algorithm on the Dehaze- Helmet dataset is 95. 7%, which is 2. 3% higher than the YOLOv9 algorithm.