# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Dataset** & **Task** & **Utilized Item** & **Result** \\ \hline
[24] & Their own & Nameplate detection & Nameplate & AP: 73.2\% \\
[25] & Their own & Nameplate recognition & Nameplate & Accuracy: 96.4\% \\
[26] & Their own & Nameplate detection & Nameplate & AP: 90.1\% \\
[27] & - & Positioning transmission line tower & POS & Positioning accuracy within 5 m \\
[28] & - & Point cloud positioning & POS & Build up the cable inspection robot \\  & & & & motion trajectory model \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Literature review of nameplate and POS applications in power transmission line inspection. exist within a small vicinity. When matching the identity of transmission towers, certain features that are not successfully matched might be the critical indicators differentiating transmission towers' identities. Consequently, utilizing local feature matching methods may result in subpar matching outcomes. Global image features represent the overall content of an image. Convolutional Neural Networks (CNNs) progressively extract and integrate local features into a global context through their hierarchically increasing receptive fields and stacked structures, capturing both the fine details and overall structure of images. In recent years, due to their significant feature extraction capabilities, numerous global feature matching algorithms have begun to utilize CNN to extract global image features. Facenet [35] employs a CNN-based architecture and introduces a triplet loss training mechanism for facial matching tasks. [PERSON] et al. [36] employ Convolutional Neural Networks for feature extraction and append a verification sub-network based on a classification sub-network to address the pedestrian re-identification problem. Arcface [37] uses CNN for input image feature extraction, considers angles in the feature space as classification boundaries, and incorporates the Arcface loss for model training, further enhancing the accuracy of face classification. The networks mentioned previously utilize CNNs to extract global image features, and corresponding experiments have demonstrated the reliability of their global feature extraction methods. The reviewed studies offer vital perspectives on the identification of transmission towers  
1. Defect inspection in electrical power systems, which is vital, tends to favor the use of single-stage identification algorithms from the YOLO series. These algorithms not only excel in terms of accuracy but also demonstrate a remarkable recognition speed, meeting the practical demands and standards of electrical inspections;
2. The identification matching of transmission towers is crucial, facilitating the maintenance of the towers and their components. Manual comparison methods are time-consuming and can potentially be influenced by human factors; nameplate recognition and POS positioning represent effective and mature methods for matching transmission towers in the automated inspections. However, nameplate recognition is incapable of handling situations where images lack nameplates. Additionally, POS have high installation and maintenance costs, and their matching effectiveness diminishes when GPS data are unavailable. 3. Local feature matching algorithms have limitations, as models tend to match similar features but often overlook unmatched critical features;
4. Convolutional Neural Networks (CNNs) can extract and integrate global image features. When combined with different head networks, they have been applied in pedestrian re-identification and face matching. Transmission towers are typically located in vast open areas and surrounded by the same type of transmission towers, which exhibit similar external shapes. In the task of transmission tower re-identification, it is possible to encounter multiple instances of the same type of transmission towers within a single image. Consequently, the spatial relationship of key points among these towers might be identical.