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 DeDi-Transformer(Density Difference-Transformer)was proposed.The algorithm used density contrast to realize density perception,enhanced the collected working face monitoring image,and improved the clarity of the personnel's safety helmet in the image.Secondly,in view of the problem that it was difficult for the coal mine working face monitoring system to quickly and accurately identify whether coal miners were wearing safety helmets,a safety helmet recognition algorithm based on SAC-YOLOv9(Supervised Atrous Convolution-YOLOv9)was proposed.The algorithm added the supervised atrous convolution into the YOLOv9 backbone extraction network to obtain receptive fields of different scales,speeded up feature extraction,and improved 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 0.717 9,which is 0.043 4 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.
维普
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