Research on YOLOv8 identification method of grid insulator defects caused by natural disasters

Insulators in the power system are easily aged and damaged when exposed to harsh environments for a long time, which may lead to power transmission system failures. Regular inspection is crucial to the stable operation of the system. Traditional manual inspection has low efficiency and high security risk. With the development of UAV technology, inspection based on UAV aerial images has gradually become the mainstream. However, due to the small insulator defect target and complex background, traditional image processing is difficult to meet the needs. Therefore, this paper proposes an insulator defect detection method based on YOLOv8 model, which realizes efficient detection under complex background through deep learning model. The open source data set and data enhancement technology are used to build high-quality data sets. Experimental results show that the recall rate of this method is 97.56% and [email protected] 98.86%, which has strong generalization ability and robustness. It provides an intelligent solution for power inspection and has important application value.