首先针对煤矿工作面喷雾除尘场景下监控系统采集到的图片模糊, 清晰度低的问题, 提出了一种基于DeDi-Transformer (Density Difference-Transformer)的煤矿工作面图像去雾算法, 该算法利用密度差实现密度感知, 对采集的工作面监控图像进行增强, 提高图像中人员安全帽的清晰度; 其次针对煤矿工作面监控系统很难快速准确识别出煤矿工人是否佩戴安全帽的问题, 提出了一种基于SAC-YOLOv9 (Supervised Atrous Convolution-YOLOv9)的安全帽识别算法, 该算法在YOLOv9主干提取网络中加入监督空洞卷积, 获取不同尺度的感受野, 加快特征提取, 提高安全帽识别的精度。实验结果表明, DeDi-Transformer算法在Braize-Haze数据集上的PSNR为19. 85 dB, 比DeHamer算法提升了2. 49 dB; SSIM是0. 717 9, 比DeHamer算法提高了0. 043 4。SAC-YOLOv9算法在Dehaze-Helmet数据集上的mAP是95. 7%, 与YOLOv9算法相比提升了2. 3%。

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.