Hidden danger identification in UAV inspection images of overhead transmission lines

Abstract

[Objective]In the power system, overhead transmission lines are a critical link in the transmission of electrical energy, and their safe and stable operation is crucial. However, with the continuous changes in the natural environment and rapid growth of vegetation, trees in transmission line corridors have become one of the main hidden dangers affecting line safety. The high proximity between trees and transmission lines may not only cause faults such as short circuits and tripping but also lead to fires in severe cases, posing a serious threat to the safety of the power grid and people's lives and property. Therefore, to improve the accuracy of identifying tree obstacles, this paper designed a method to identify tree obstacles for overhead transmission lines based on unmanned aerial vehicle (UAV) inspection images. [Methods]To improve the quality of UAV inspection images, histogram equalization was used to enhance the contrast of the images, making the detailed information in the images clearer. The use of transformation functions further enhances the edge features of the images, laying the foundation for subsequent feature extraction. The FROST filter was used to remove image noise, ensuring accuracy of subsequent processing while preserving edge details. The images were smoothed using binarization methods, and the color features of tree obstacles and the texture features of conductor sag of the transmission line were extracted from the inspection images. In response to the missing edge information in images due to factors such as shooting angle and lighting, an interpolation algorithm was used to supplement the missing image edge values, ensuring the integrity of feature extraction. On this basis, the Euclidean distance between adjacent data was calculated to obtain the annotation results of feature fusion. Consequently, hidden dangers in overhead transmission line corridors were identified. [Results]The experimental results show that the proposed method performs well in the task of identifying hidden dangers brought by tree obstacles for the overhead transmission lines. It not only accurately identifies 5 areas of hidden dangers from tree obstacles but also has a small error between the identification results and the actual number of hidden dangers due to tree obstacles, demonstrating excellent identification ability. In the accuracy analysis of the location coordinates of hidden dangers, the proposed method identifies coordinates of (1.43 m, 8.3 m) and (1.49 m, 9.8 m) in areas b and d, respectively, which are closest to the actual data. This proves the high accuracy of the proposed method in identifying the actual distance of tree obstacle areas. In addition, compared to the other methods, the proposed method has more accurate identification results for various levels of hidden dangers brought by tree obstacles, and the values are closer to the actual situation in the experimental area, which verifies its superiority and reliability in practical applications. [Conclusion]The proposed method can effectively identify the hidden danger areas of transmission lines and accurately judge the number and characteristics of hidden dangers, having high practicability. From the above results, it can be seen that the proposed method combines UAV inspection images with advanced image processing technology to achieve automated and intelligent identification of tree obstacles in overhead transmission line corridors. In addition, by integrating color features and texture features, the accuracy and robustness of identification have been improved. This research achievement is of great significance for improving the safety and stability of the power system and has made positive contributions to promoting the construction and development of smart grids.