# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 1 Introduction
Their research is summarized in Table 2.  
Figure 1: Challenges in nameplate identification: (**a**) the nameplate is situated in an occluded zone; (**b**) nameplate detachment; (**c**) nameplate damage; (**d**) nameplate indistinctness. Image matching techniques have been widely adopted in various subdisciplines. In the realm of facial recognition, matching algorithms are designed to precisely identify specific individuals from extensive facial image databases. For pedestrian re-identification, matching algorithms strive to accurately recognize the same individual from different camera perspectives and at different time intervals. Moreover, in landmark recognition, matching algorithms have proven their value, adeptly identifying specific structures or locations from a plethora of scene images, significantly underpinning tourism landmark identification and autonomous driving technologies. At present, the features employed in image matching algorithms primarily encompass local and global characteristics. Local feature matching predominantly relies on feature extraction and descriptor matching. These techniques initially extract salient point or line features from images, compute descriptors for these features, and then match images pairs based on descriptor similarity. Traditional local image feature matching approaches include ORB [29] and LBD [30], among others. ORB integrates FAST key point detection with BRIEF descriptors, incorporates rotational invariance, conserves computational resources, and is frequently employed in visual localization and map reconstruction. LBD is a local line feature matching algorithm that accumulates edge responses to generate surrounding regions of line segments and formulates a one-dimensional descriptor, and is commonly utilized for architectural and urban landscape image matching. [PERSON] et al. [20] enhanced the ORB point matching and LBD line matching algorithms to address the pose estimation of UAV relative to transmission towers. [PERSON] et al. [31] refined the ORB algorithm to extract uniform key points and, in conjunction with the LSD [32] algorithm, matched regions of transmission towers and backgrounds in images to estimate the UAV pose. Deep-learning-based local feature matching algorithms encompass methods like SuperPoint [33] and SuperGlue [34]. SuperPoint, grounded on convolutional neural networks, is designed for key point detection and descriptor generation. SuperPoint is typically employed in image registration and 3D reconstruction tasks, utilizing Euclidean distance and cosine similarity as matching assessment metrics. SuperGlue, a graph neural-network-based matching method, necessitates key points and descriptors as inputs. Through its graph neural network and optimized matching layer, SuperGlue facilitates inter-image matching and is prevalently used in visual SLAM and robotic navigation tasks. Local feature matching techniques can achieve image local feature matching, subsequently enabling the tracking of dynamic object movement trajectories and pose estimation. However, the task of transmission tower identity matching is to distinguish between different individual transmission towers. In practice, multiple similar transmission towers may \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Method Category** & **Advantages** & **Disadvantages** \\ \hline - & Manual comparison & High reliability & High labor intensity and low work efficiency \\
[24; 25; 26] & Nameplate detection and & High precision & The nameplate may fall off and become invisible \\
[27; 28] & POS & High precision and & High setup and maintenance costs; positioning \\  & & real-time positioning & failure under weak GPS signals. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The advantages and disadvantages of the three methods.