Abstract
Automatic inspection of insulators from high-voltage transmission lines is of paramount importance to the safety and reliable operation of the power grid. Due to different size insulators and the complex background of aerial images, it is a difficult task to recognize insulators in aerial views. Most of the traditional image processing methods and machine learning methods cannot achieve sufficient performance for insulator detection when diverse background interference is present. In this study, a deep learning method—based on You Only Look Once (YOLO)—will be proposed, capable of detecting insulators from aerial images with complex backgrounds. Firstly, aerial images with common aerial scenes were collected by Unmanned Aerial Vehicle (UAV), and a novel insulator dataset was constructed. Secondly, to enhance feature reuse and propagation, on the basis of YOLOv3 and Dense-Blocks, the YOLOv3-dense network was utilized for insulator detection. To improve detection accuracy for different sized insulators, a structure of multiscale feature fusion was adapted to the YOLOv3-dense network. To obtain abundant semantic information of upper and lower layers, multilevel feature mapping modules were employed across the YOLOv3-dense network. Finally, the YOLOv3-dense network and compared networks were trained and tested on the testing set. The average precision of YOLOv3-dense, YOLOv3, and YOLOv2 were 94.47%, 90.31%, and 83.43%, respectively. Experimental results and analysis validate the claim that the proposed YOLOv3-dense network achieves good performance in the detection of different size insulators amid diverse background interference.
Highlights
With the development of computer vision techniques and intelligent grids, the scale of high-voltage transmission lines is increasing
The basic configuration of the PC used in this experiment was as follows: Intel(R) Core(TM) i9-9900 K (Intel, Santa Clara, CA, USA), 3.6 GHz primary frequency of CPU, 32 G of RAM, NVIDIA GeForce GTX 3080 (10 G) graphics card (Intel, Santa Clara, CA, USA), CUDA 11.1 and cuDNN 8.0.5 accelerated environments, with Visual Studio 2017 and Open CV 3.4.0 adopted as the visual studio framework
A novel insulator dataset was constructed, which contained composite insulator images captured by Unmanned Aerial Vehicle (UAV) in common aerial scenes
Summary
With the development of computer vision techniques and intelligent grids, the scale of high-voltage transmission lines is increasing. Regular inspection of transmission lines is becoming an important task to ensure the safety and reliable operation of power systems. Insulator failure is likely to threaten the safety of the power system, resulting in large-scale blackouts and huge economic losses. Over the past few years, the development of Unmanned Aerial Vehicle (UAV) and sensor techniques [4,5] has led to their exploitation as effective tools for transmission line inspection. Insulator detection by UAVs has become one of the primary research directions for intelligent grid systems [6]. Some scholars have engaged in research on insulator detection from aerial images, and many remarkable results have been achieved through image processing. Current methods for insulator detection can be divided into three categories: (1) traditional image processing methods, (2) machine learning methods, and (3) deep learning methods
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