# Transmission Tower Re-Identification Algorithm Based on Machine Vision
## 3 Experiment and Analysis
### Experiment on Transmission Tower Identity Matching
#### 3.3.2 Model Evaluation and Result Analysis  
Utilizing 337 images from the test dataset, for the evaluation of the model, we proceed in two ways. On one hand, we compare the performance of different networks under the same feature dimension (d), and on the other hand, we compare the performance of the same network under different feature dimensions (d). The evaluation of the matching model is conducted using Server 2.  
This paper utilizes the training set from the transmission tower matching dataset to establish a template library for transmission tower matching. For matching networks constructed with different backbone networks, when the mapping dimension d is set to 128, the accuracy of the models on the test set is shown in Table 6. Three distinct backbone architectures were integrated into the matching network. Upon adopting the proposed triplet online generation strategy combined with the GPS preliminary filtering, enhancements were observed in both rank-1 and mAP_rank-3. With the exclusive integration of the triplet online generation strategy, Inception-Resnet-v1 exhibited the highest matching precision, registering a rank-1 score of 89.32 and a mAP_rank-3 of 90.31. When solely employing the GPS preliminary filtering, MobileNet demonstrated superior matching precision, achieving a rank-1 score of 88.72 and a mAP_rank-3 of 90.01. Incorporating both the triplet online generation strategy and GPS preliminary filtering, Inception-Resnet-v1 sustained its leading performance, with a rank-1 score of 89.32 and a mAP_rank-3 of 90.31. Furthermore, after deploying GPS preliminary filtering, the inference speed of all models was improved.  
Figure 15: Comparison of training effects in the matching network: (a) validation loss curve; (b) validation accuracy curve. (c) train loss curve.  
The aforementioned experimental findings elucidate that the application of triplet mining markedly elevates the rank-1 accuracy of models, a fact most prominently observed in the Inception-ResNet-v1 based matching network, which attains the peak rank-1 accuracy subsequent to the mining implementation. Delving into the effects of the feature dimension d, as generated by the matching network, this investigation assesses the impact of varying d values on the network's efficacy, predicated based on the integration of online triplet generation. As delineated in Table 7, a comparative analysis of network performance, utilizing Inception-ResNet-v1 as the backbone for d values of 128 and 256, reveals enhancements in rank-1, mAP_rank-3, and mAP metrics upon increasing d from 128 to 256. Nevertheless, in instances where GPS information is incorporated within the input images, the model experiences a processing deceleration of 16.4 ms per image when the feature dimension d is increased to 256, compared to d = 128. In the absence of GPS data, this delay extends to 40.5 ms per image. Therefore, setting d to 256 is suitable for processing inspection images on local servers, while setting d to 128 is more appropriate for deployment on edge devices such as UAVs.