赵振兵,蒋志钢,李延旭,戚银城,翟永杰,赵文清,张珂(华北电力大学电子与通信工程系, 保定 071003;华北电力大学河北省电力物联网技术重点实验室, 保定 071003;华北电力大学控制与计算机工程学院, 保定 071003)
Overview of visual defect detection of transmission line components
Zhao Zhenbing,Jiang Zhigang,Li Yanxu,Qi Yincheng,Zhai Yongjie,Zhao Wenqing,Zhang Ke(Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China;Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, China;School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China)
With the continuous improvement of China's economic strength and people's living standards, the requirements of the state and the people for electric power are gradually improving. To meet the increasing demand for electricity, the grid system is constantly developing, leading to increased time and capital costs required for the safe operation and maintenance of power grids. The rise of unmanned aerial vehicle (UAV) technology has introduced new detection ideas, which make the intelligent and efficient detection of defects of transmission line components a reality. Compared with manual inspection, UAV has advantages of low cost, high efficiency, strong mobility, and high safety. Thus, it has gradually replaced manual inspection. At the same time, artificial intelligence (AI) technology based on deep learning is also developing rapidly, and the related technology of applying AI to the maintenance of power equipment has developed rapidly in recent years. However, how to accurately and efficiently detect the visual defects of transmission line components is a key problem to be solved. Early component visual defect detection methods based on image processing and feature engineering have high requirements on image quality, and designing features for various transmission line components consumes much time and money. The current UAV aerial photography technology cannot meet the requirements of image quality, and its detection accuracy cannot meet the actual requirements of defect inspection of basic transmission line components. Thus, applying the component visual defect detection method based on image processing and feature engineering to complex real-life scenes is impossible. With deep learning, transmission line component defect detection models based on deep learning can effectively extract transmission line component objects and defects from aerial images with complex backgrounds. Deep learning-based detection models have many other advantages. 1) Deep learning can automatically extract multi-level, multi-angle features from original data instead of artificial design. 2) Deep learning has strong generalization and expression capabilities, that is, it possesses translation invariance. 3) Deep learning is more adaptable to complex real-world environments than traditional techniques. Therefore, the object detection model based on deep learning is an inevitable choice for processing transmission line inspection images. Before applying a deep learning model to the defect detection of key components of transmission lines, a complete defect data set of components should be created for the training of the deep learning model. However, in transmission line component defect detection, no data set is available to the public. This work aims to review the visual defect detection methods of transmission line components. On the basis of extensive research on the visual defect detection of transmission line components, existing detection methods are summarized and analyzed. First, the visual defect detection technology of key parts of transmission lines is described based on traditional algorithms. The development process of deep learning is reviewed, and the advantages and disadvantages of deep learning in defect detection are analyzed. Second, the status of research on the positioning and defect detection of three important components on transmission lines(i.e., insulator, metal, and bolt)is introduced. Third, several key problems, such as sample imbalance, small object detection, and fine-grained detection,in transmission line component defect detection are analyzed. Lastly, the future development trend of transmission line component defect detection technology that meets the requirements of complex-scene grid inspection and fault diagnosis criteria is analyzed. The conclusion is that the development of visual defect detection of transmission line components cannot be separated from the development of deep learning in the field of image processing and image data augmentation. In short, the establishment of a high-precision, high-efficiency, strongly intelligent, multi-level, full-coverage defect detection model of key components of transmission lines on the basis of deep learning remains unrealized.