# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 3 Experiment and Analysis
### Experiment on Transmission Tower Identity Matching
#### 3.3.3 Visualization of Matching Network Results  
When the matching network generates 'features1' as a 128-dimensional feature vector, the use of a matching network with Inception-ResNet-v1 (d = 128) as its backbone demonstrated a robust overall performance on the dataset used in this study. Additionally, although the use of a matching network with Inception-ResNet-v1 (d = 256) as its backbone slightly surpasses its counterpart with d = 128 in terms of the accuracy metrics, the longer inference time of the former makes it less suitable for optimization and deployment on edge devices. Given our aim to deploy the algorithm directly on edge devices in the future, we chose Inception-ResNet-v1 (d = 128) as the backbone network for our transmission tower re-identification system. For a more in-depth performance analysis, this paper considered the impact of online triplet generation strategy. In Figure 16, the numbers above each \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & **Triplet Generation** & \multirow{2}{*}{**GPS**} & \multirow{2}{*}{**Rank-1 (\%)**} & **mAP\_Rank-3** & **Inference** & \multirow{2}{*}{**mAP\_r (\%)**} \\  & **Online (Mining)** & & & & **(\%)** & **(Rank-1)/ms** & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} MobileViT-XXS \\ (d = 128) \\ \end{tabular} } & \(\surd\) & \(\surd\) & **87.24** & **87.81** & 35.1 & **88.22** \\  & \(\surd\) & & 86.65 & 87.36 & 71.2 & 87.59 \\  & & \(\surd\) & 86.35 & 87.56 & **34.8** & 86.40 \\  & & & 85.16 & 86.47 & 71.9 & 84.86 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} MobileNet \\ (d = 128) \\ \end{tabular} } & \(\surd\) & \(\surd\) & **89.32** & **90.08** & **29.5** & 89.69 \\  & \(\surd\) & & 88.43 & 89.14 & 68.0 & 88.89 \\  & & \(\surd\) & 88.72 & 90.01 & 29.9 & **90.53** \\  & & & 88.13 & 89.37 & 68.4 & 89.89 \\ \hline Inception- & \(\surd\) & \(\surd\) & **89.32** & **90.31** & **33.3** & 85.98 \\ Resnet-v1 & \(\surd\) & & 89.32 & 90.21 & 70.3 & 85.04 \\ (d = 128) & & \(\surd\) & 86.35 & 87.66 & 34.5 & **88.24** \\  & & & 85.46 & 86.92 & 69.6 & 87.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative results in the test set for matching algorithms with varying backbone networks (d = 128). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & **Triplet Generation** & \multirow{2}{*}{**GPS**} & \multirow{2}{*}{**Rank-1 (\%)**} & **mAP\_Rank-3** & **Inference** & \multirow{2}{*}{**mAP\_r (\%)**} \\  & **Online (Mining)** & & & **(\%)** & **(Rank-1)/ms** & \\ \hline Inception-Resnet-v1 & \(\surd\) & \(\surd\) & **89.32** & **90.31** & **33.3** & **85.98** \\ (d = 128) & \(\surd\) & & 89.32 & 90.21 & 70.3 & 85.04 \\ \hline Inception-Resnet-v1 & \(\surd\) & \(\surd\) & **91.39** & **91.62** & **49.7** & **90.70** \\ (d = 256) & \(\surd\) & & 90.50 & 91.15 & 110.8 & 89.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative results on the test set for matching algorithms under different feature dimensions (d = 128, d = 256). image (e.g., 0.4383) show the Euclidean distance between its 'features1' and the query's 'features1'. Smaller values denote higher similarity. \"tower'\(i\)'' indicates its sequence as the \(i\)th transmission tower instance. The search results from left to right represent the decrease in image similarity (top-1, top-2, top-3). As illustrated in Figure 15(a),b, the online triplet generation strategy can notably reduce distances between images of the same tower, thereby enhancing the rank-1 accuracy of the matching network. In this study, a subsequent data collection was conducted six months later for a subset of transmission towers. As Figure 15(c) illustrates, changes in the environment led to some mismatches between the newly collected images of these towers and those in the database.