Editorial Department of Power Generation Technology
Doi
Abstract
ObjectivesTraditional power grid inspection methods suffer from high labor intensity and low efficiency. Taking Shandong Golden Power Grid as the research object, this study proposes an inspection algorithm using unmanned aerial vehicle (UAV) based on lightweight deep learning network YOLOv5-Mv3 for detecting grid insulators and foreign objects.MethodsFirstly, a dataset is constructed using images captured by UAVs during power grid inspection and is trained. Then, for the grid insulators and foreign objects, Mobilenetv3 is used to replace CSPDarknet53 as the feature extraction network in order to lighten the YOLOv5-Mv3 model, reducing parameters and computational cost while maintaining accuracy and enabling real-time detection.ResultsThe proposed detection algorithm achieves a mean Average Precision of 84.7% and 56.6 frames per second. Compared to Faster RCNN, SSD, and YOLOv4 models, the improved YOLOv5-Mv3 demonstrates higher detection accuracy and faster performance.ConclusionsThe proposed algorithm improves the efficiency of UAV-based power grid inspection and achieves lightweight and high-efficiency effect, fully meeting the requirements for intelligent power grid inspection
