Abstract
The accurate and timely abnormal object detection is of crucial importance for the safe operation of power grid. It is rather difficult, however, to completely manually recognize such objects based on the uploaded pictures in the cloud server. To meet the demand of accuracy and timeliness, this paper proposes to combine the cloud/edge fusion framework and deep learning techniques for abnormal object detection. Specifically, we first train the model of abnormal object detection by using YOLOv4 in the cloud server, and then apply the trained model to detect whether there is an abnormal object for each captured picture in edge servers. As the data sample is not very large at the early stage of the system, we use some enhancement techniques to enlarge the number of pictures, and afterwards new real-time data streams are also used for incremental learning. Our experiments show that the proposed framework can accurately and timely detect the abnormal objects near power transmission lines.
Highlights
T He security and reliability of power transmission lines are of crucial importance to the stability of a smart grid
We first train the model of abnormal object detection by using YOLOv4 in the cloud server, and apply the trained model to detect whether there is an abnormal object for each captured picture in edge servers
Some universal features are used in YOLOv4, which include Weighted-ResidualConnections (WRC), Cross-stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarialtraining (SAT) and Mish-activation, Mosaic data augmentation, DropBlock regularization, and CIoU loss, and it can achieve a high-accuracy of 43.5% AP for the MS COCO dataset at a real time speed
Summary
T He security and reliability of power transmission lines are of crucial importance to the stability of a smart grid. Edge computing [4], [5], termed as cloudlet, fog computing, and edge-clouds, extends data processing to the network edges with close proximity to the data producers, i.e., IoT devices It enjoys the advantages of low latency, saving bandwidth, protecting data privacy, etc., and is more feasible for applications which require realtime decision-making action on-site, as opposed to the cloud computing. As model training using deep learning techniques (especially for object detection) often requires high computational resource, we shift the training task to the cloud server and use the trained model for abnormal object detection in the edge servers when receiving uploaded images from terminal devices. As the inference operation is conducted in the edge server near to terminal devices, and only images where an abnormal object is detected by the model are uploaded to the cloud server, the bandwidth can be greatly saved and the results can be quickly obtained by the system administrator.
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