Cloud-Edge Fusion Based Abnormal Object Detection of Power Transmission Lines Using Incremental Learning

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.