Abstract
Defective insulators seriously threaten the safe operation of transmission lines. This paper proposes an insulator defect detection method based on an improved YOLOv4 algorithm. An insulator image sample set was established according to the aerial images from the power grid and the public dataset on the Internet, combining with the image augmentation method based on GraphCut. The insulator images were preprocessed by Laplace sharpening method. To solve the problems of too many parameters and low detection speed of the YOLOv4 object detection model, the MobileNet lightweight convolutional neural network was used to improve YOLOv4 model structure. Combining with the transfer learning method, the insulator image samples were used to train, verify, and test the improved YOLOV4 model. The detection results of transmission line insulator and defect images show that the detection accuracy and speed of the proposed model can reach 93.81% and 53 frames per second (FPS), respectively, and the detection accuracy can be further improved to 97.26% after image preprocessing. The overall performance of the proposed lightweight YOLOv4 model is better than traditional object detection algorithms. This study provides a reference for intelligent inspection and defect detection of suspension insulators on transmission lines.
Highlights
As the basic component of transmission lines, suspension insulators have to withstand the vertical and horizontal loads and the tension of conductors, and withstand the working voltage and over voltage, so they must have good electrical insulation and mechanical properties
unmanned aerial vehicle (UAV) inspections will generate a large amount of image information, and the manual judgment by inspectors is inefficient and easy to cause false recognition, while deep learning technology can meet the needs of intelligent processing and analysis of massive inspection images, which is an effective method to achieve automatic identification of transmission line defects
This paper proposes a transmission line insulator defect detection method based on the improved YOLOv4 algorithm
Summary
As the basic component of transmission lines, suspension insulators have to withstand the vertical and horizontal loads and the tension of conductors, and withstand the working voltage and over voltage, so they must have good electrical insulation and mechanical properties. Wang et al [7] proposed a method to identify ice thickness of transmission lines based on MobileNetV3 feature extraction network and SSD detection network, which can reach an accuracy of 74.5%. This paper proposes a transmission line insulator defect detection method based on the improved YOLOv4 algorithm. The results show that the method proposed in this paper has high detection accuracy and faster speed, which can provide a reference for real-time detection of transmission line insulator defects. In order to obtain a more robust, stable, and generalizable deep learning model, the GraphCut segmentation algorithm was used to extract defective insulator targets, which were blended with the background image of practical power grid environment.
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