Online insulator defects detection and application based on YOLOv7-tiny algorithm

Abstract

As an indispensable part of the power transmission system, insulators are of great importance to the safe and stable operation of power grids in terms of their healthy and reliable operation. To realize real-time monitoring of insulator defects under a complex environment, this paper proposes an insulator defect detection method based on the You Only Look Once version 7-tiny (YOLOv7-tiny) algorithm. Then an edge device-unmanned aerial vehicle (UAV) inspection system is developed to verify the real-time performance of the algorithm. By introducing the structure intersection over union (SIoU) loss function to the YOLOv7-tiny model, the regression speed of the anchor frame can be effectively accelerated on the basis of the miniature model, to accelerate the model operation. Thereafter, a smooth sigmoid linear unit (SiLU) activation function is used in the network neck to improve the nonlinear representation ability; After that, an edge computing device based on NVIDIA Jetson Xavier NX is established to verify the real-time performance of the method. Experimental results reveal mean average precision (mAP) of insulators and their missing series defects is as high as 98.31%. Besides, the detection speed of the designed model deployed to mobile edge devices can reach 35 frames per second (FPS), with real-time and accurate detection performance of insulators and their missing series defects.