Gong-kuang zidonghua (Dec 2021)
Real-time video processing system in coal mine based on edge-cloud collaborative framework
Abstract
At present, the intelligent video monitoring in coal mine mainly adopts cloud computing to process real-time video, and the video transmission occupies large network resources and has high delay, which can not respond to emergency events in the monitoring area in real time. In order to solve this problem, a real-time video processing system in coal mine based on edge-cloud collaborative framework is proposed. In this system, the real-time target recognition task is sent to the edge, and the tasks with large calculation and weak real-time performance such as edge device integration are sent to the cloud for processing. At the video monitoring site, the neural network model deployed on the edge device is used to process the video monitoring image locally. Through the underground heterogeneous fusion network, the processing results and model parameters of the edge devices in different network environments are sent to the cloud server. The cloud server updates and pushes the model of edge devices in each scene, and finally realizes real-time interaction of edge-cloud data and online optimization of edge services. In order to solve the problems that Tiny-YOLOv3 can not extract the deep characteristic of the image, and is prone to gradient disappearance and over-fitting, a down-sampling residual module is designed according to the residual structure, and Tiny-YOLOv3 is improved to improve the deep characteristic extraction and generalization capability of the model. On the basis of edge-cloud data interaction, the target detection model on the edge device is optimized for the targeted scene to improve the accuracy of model detection on the edge device. The test results show that the stability and data generalization capability of the improved Tiny-YOLOv3 model are better than those of YOLO and Tiny-YOLOv3. After specialized training in a single scene, the improved Tiny-YOLOv3 model is more accurate in target recognition. Compared with cloud computing, the edge-cloud collaborative framework can reduce the latency of monitoring video processing significantly.
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