Management of power equipment inspection informationization through intelligent unmanned aerial vehicles

Abstract

With the implementation of intelligent unmanned aerial vehicles (UAVs) in power equipment inspection, managing the obtained inspection results through information technology is increasingly crucial. This paper collected insulator images, including images of standard and self-exploding insulators, during the inspection process using intelligent UAVs. Then, an optimized you only look once version 5 (YOLOv5) model was developed by incorporating the convolutional block attention module and utilizing the efficient intersection-over-union loss function. The detection performance of the designed algorithm was analyzed. It was found that among different models, the YOLOv5s model exhibited the smallest size and the highest detection speed. Moreover, the optimized YOLOv5 model showed a significant improvement in speed and accuracy for insulator detection, surpassing other methods with a mean average precision of 93.81% and 145.64 frames per second. These results demonstrate the reliability of the improved YOLOv5 model and its practical applicability.