# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 1 Introduction
[21] took into account the compositional relationship of transmission tower components and enhanced YOLOX for defects detection of the towers and their components. Post-modification, there was a 10.13% improvement in mean average precision (mAP) compared to the original YOLOX. Additionally, algorithms from the YOLO series, such as YOLOv5 and YOLOv7, have been applied in the detection of electrical system equipment and in defect detection [22; 23]. Currently, there are three main methods for transmission tower identity matching, that is, manual comparison, nameplate recognition and precise positioning using position and orientation system (POS). For manual comparison, the identification of transmission towers primarily depends on inspection staff using information from the transmission tower nameplates and their environmental surroundings. Nevertheless, this methodology is characterized by considerable labor intensity and suboptimal efficiency in the inspection processes. For transmission tower identity matching, the recognition of the nameplate represents a prevalent approach. [PERSON] et al. [24] detected the transmission tower nameplate using Faster R-CNN and subsequently proposed, in her undergraduate thesis, an adversarial-learning-based Super-Resolution Feature Generation Network (SFGNet) for enhancing the accuracy of small object detection. [PERSON] et al. [25] applied affine distortion correction to digital text images and recognized numbers on the transmission tower nameplates usingthe CRNN algorithm. [PERSON] et al. [26] constructed the F3 RNet for the recognition of electrical equipment, attaining an average precision (AP) of 90.1% in the detection of transmission tower nameplates. In practice, relying solely on the transmission tower nameplate for transmission tower re-identification can introduce an array of challenges. For instance, the placement of transmission tower nameplate varies across different transmission towers. In the photographic process targeting the transmission towers, the transmission tower nameplate may reside in the camera's occluded region, becoming imperceptible (Figure 1a); Additionally, the transmission tower nameplate might have fallen off or been damaged due to harsh weather conditions like snowstorms (Figure 1b,c); Due to prolonged outdoor exposure, the transmission tower nameplate has become illegible (see Figure 1d). Precise positioning methods employing the Position and Orientation System (POS) require the use of high-accuracy GPS data. [PERSON] et al. [27] chose to use the Position and Orientation System (POS) to address the transmission line tower matching. [PERSON] et al. [28] utilized POS to acquire the coordinates of the scene's point cloud and the precise movement trajectory of the cable inspection robot. Typically, to obtain high-accuracy GPS data, it is necessary to deploy Differential GPS (DGPS), which enhances the accuracy of standard GPS using ground-based reference stations. When transmission towers are located in remote areas, the system needs to establish multiple stations for comprehensive coverage. Additionally, during the inspection process, the GPS receiver is typically integrated into the inspection device, and it is necessary to deploy sensors such as Inertial Measurement Units (IMUs) and magnetometers to deduce the GPS positions of objects captured in the imagery. Consequently, the deployment of the POS incurs significant expenditures and poses considerable challenges in terms of maintenance. Furthermore, GPS signals can be affected or even lost in dense urban areas, tunnels, mountainous regions, and high-humidity weather conditions. The advantages and disadvantages of the three methods are delineated in Table 1. In recent years, researchers have detected and recognized transmission tower nameplates for matching towers, while others have utilized POS for the positioning of transmission towers and their components.