Abstract
The normal operation of transmission lines is one of the basic guarantees for industrial development. The foreign object on the transmission line is one of the main reasons for the failure of the transmission line, which brings huge economic losses to the society. The traditional method has the problem of low accuracy and slow speed. The detection and identification of foreign objects on transmission lines are beneficial to the maintenance of huge economic benefits. Therefore, cloud-edge technology is introduced into this study, and a monitoring model for foreign objects on transmission lines in complex scenarios based on an improved YOLOv4 network is proposed. The feature extraction module of the YOLOv4 model is improved based on the GhostNet network to speed up the detection of foreign objects. The SPP module in the YOLOv4 network is improved to enhance the accuracy of detection and identification of foreign objects on transmission lines. The accuracy and speed of the detection of foreign objects tested are 99.2% and 218ms, respectively. The experimental results can meet the needs of real-time detection.
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