基于边缘计算和ST-YOLO的矿井智能监控技术研究

Mine intelligent monitoring technology based on edge computing and ST-YOLO

矿井视频监控在保障煤矿企业生产安全方面有着关键作用。目前矿井智能监控技术主要在云端进行监控数据处理,存在着网络拥塞,计算要求高等问题。针对该问题,研究了边云协同矿井视频监控总体架构,提出了基于任务卸载的自适应视频帧卸载策略,利用边缘端检测速度快、精度高、实时性强的优点,进行模型的优化更新,实现边云协同架构的不断完善。针对YOLOv5模型检测精度低,深层网络结构易发生梯度消失和过拟合的问题,Transformer应用到视觉领域面临同一种目标的多尺度无法识别和高分辨率图像构成的序列过长,计算量大,显存资源不足的问题,构建了基于Swin Transformer-YOLOv5的目标检测模型。实验结果表明,基于ST-YOLOv5的目标检测模型,提高了平均检测精度,适用于矿井智能工作面边缘端设备部署。

Mine video surveillance plays a key role in ensuring the production safety of coal mining enterprises.At present,mine intelligent monitoring technology mainly processes monitoring data in the cloud,which has problems such as network congestion and high computing requirements.In response to this problem,the overall architecture of edge-cloud collaborative mine video monitoring was studied,and an adaptive video frame offloading strategy based on task offloading was proposed.The advantages of edge detection speed,high accuracy,and strong real-time performance were used to optimize and update the model,realizing continuous improvement of edge-cloud collaboration architecture.In view of the low detection accuracy of the YOLOv5 model,and the deep network structure is prone to gradient disappearance and over-fitting,Transformer is applied to the visual field and faces the problem of multi-scale unrecognizability of the same target and the sequence of high-resolution images is too long and requires a large amount of calculation.Due to the problem of insufficient video memory resources,a target detection model based on Swin Transformer-YOLOv5 was constructed.Experimental results show that the target detection model based on ST-YOLOv5 improves the average detection accuracy and is suitable for the deployment of edge devices on mine intelligent working surfaces.