Real-time detection of UAV detection image of power line insulator bursting based on YOLOV3

Abstract

The insulator fault of high voltage line is the main factor of transmission accidents, so positioning and detection of burst insulator of power line has become an important part of routine detection. Traditional UAV detection is still mainly to evaluate the image transmitted by UAV manually, which is not only time-consuming, but also not accurate. In this paper, UAV aerial images are used to make datasets, the problem of insulator class imbalance (normal insulator and burst insulator) in the model training process is solved by using data augmentation, an improved depth learning algorithm is proposed based on YOLOv3, which provides rich semantic information for prediction layer by adding feature mapping module. At the same time, the residual network is introduced into the feature extraction, which improves the detection accuracy of small objects, more effectively extracts the object features of burst insulator, carries out real-time object detection and positioning, and completes the daily detection of insulator state of power transmission lines. The experimental results show that the detection accuracy (mAP) of the improved YOLOv3 algorithm is 91.22%, and the detection speed is 28 frames/s. The recall rate and Intersection Over Union (IOU) are also improved.