# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 1 Introduction  
Transmission towers are used to support power transmission lines. Due to prolonged exposure to outdoor environments, power systems are susceptible to various natural risks such as floods, snowstorms, and heatwaves, as well as physical defects like missing insulator pieces, loss of damper heads, and infrastructure damage [1]. All these factors can affect the normal operation of power transmission lines, leading to disruptions in power transmission, economic losses, and even threats to public safety. In recent years, with the rapid advancement of machine learning and deep learning, these technologies are increasingly being employed to predict and detect faults in power grids [2, 3]. [PERSON] et al. [4] propose an approach based on machine learning techniques to predict power gridoutages caused by heatwaves. Typically, power grids commonly utilize risk assessments to mitigate the detrimental effects of natural disasters like heatwaves and floods, thereby reducing potential damage. Simultaneously, due to the prolonged exposure of power systems to outdoor environments, various physical defects can emerge. Such defects can lead to interruptions in power transmission. The literature [5] utilizes variations in voltage and current to localize defects within the power transmission lines, but it is possible that these defects have not yet manifested as changes in the electrical parameters of the power transmission lines. If the grid system could detect these defects promptly, it would enable preemptive interventions before any deviations in voltage and current become evident, thereby reinforcing the stability and reliability of the power transmission lines. Thus, it is essential to detect and replace the defective parts. Typically, the power grid conducts inspections of its power transmission lines to detect faults [6]. Currently, the methods for inspections include manual inspection, helicopter patrols, and Unmanned Aerial Vehicle (UAV) inspections [7]. The inspections of transmission towers and their accessories primarily include: foreign object detection [8; 9], the detection of insulator and other infrastructural damage [10; 11], and checks for transmission tower tilting and damage [12]. When defects located in transmission towers and their accessories are detected during inspection, the power system should implement maintenance measures immediately. However, prior to maintenance measures, it is imperative to first determine the identity and location of the transmission tower where the damage is situated. Transmission tower re-identification refers to recognizing the transmission tower's location and specific identification, which is pivotal for their timely maintenance. Transmission tower re-identification needs to achieve object detection and identity matching for transmission towers. The primary task of transmission tower re-identification is the target recognition of the transmission towers. Traditional object detection algorithms tend to rely on hand-crafted features, such as Sobel edge detection, Haar, and HOG features. These features exhibit limited generalization capabilities, resulting in a subpar performance in complex scenarios. Object detection algorithms based on deep learning fall into two categories. One is two-stage detection algorithms reliant on region proposals, such as Fast R-CNN [13] and Faster R-CNN [14]. The other is based on regression analysis, exemplified by the YOLO [15; 16; 17] series and EfficientDet [18]. In the context of transmission tower detection, [PERSON] et al. [19] introduced DSA-YOLOv3 to enhance detection performance in aerial images of transmission towers. Experiments showed that DSA-YOLOv3 achieved a higher average precision (AP) than both YOLOv3 and the two-stage Fast-RCNN. [PERSON] et al. [20] optimized Faster R-CNN by reducing its convolutional layers, catering to the speed and accuracy requirements for transmission tower detection on mobile devices. [PERSON] et al.