# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 4 Simulation and Experiment
### Simulation
#### 4.1.3 Testing  
1. Ablation experiments  
To validate the improvement of the position-wise attention module on the feature extraction network's ability to characterize target features, we conducted an ablation experiment to assess the attention modules, as well as a comparison experiment of the attention modules. The results of the image classification task in the ImageNet dataset were used for evaluation, and four commonly used attention models, SENet, CBAM, ECA-Net, and NAM, were selected for comparison with the position information attention proposed in this part. The experimental results are shown in Table 3.  
The comparison criteria are the number of parameters, Top1 accuracy and Top5 accuracy. The number of parameters indicates the number of parameters of the network; the Top1 accuracy rate indicates the percentage of correct results with the highest score among the classification results. Experimental results show that the ILSN model with the introduction of the position-wise attention module exhibits the best performance among the six networks in terms of Top1 accuracy and Top5 accuracy, reaching 88.79% and 86.44%, with 9.31% and 7.64% improvements compared to the base MobileNetV2 network. The ILSN also improved the Top1 accuracy and Top5 accuracy by 6.23% and 5.63% compared to the ECA-Net, which was the second-best performer, indicating the practical improvement of the feature extraction network's ability to characterize target features by position-wise attention. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Size** & **Top1 Accuracy** & **Top5 Accuracy** \\ \hline MobileNetV2 & 3.51 M & 79.48 & 78.80 \\ SENet & 3.53 M & 80.23 & 79.35 \\ CBAM & 3.54 M & 80.26 & 79.34 \\ NAM & 3.51 M & 80.66 & 79.82 \\ ECA-Net & 3.51 M & 82.56 & 80.81 \\ Ours & 3.68 M & 88.79 & 86.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of comparative experiment of attention. Quantitative evaluation  
To ensure the accuracy of the dataset, the UAV123 dataset contains 12 common challenges during UAV applications, such as rapid target movement, target occlusion, low screen resolution, target size variation and screen illumination adjustment, covering most of the scenarios that will be encountered during UAV flight. This dataset thus allows for a comprehensive and unbiased assessment of the performance of tracking algorithms in the UAV environment. To quantitatively compare the algorithm performances, five mainstream target tracking algorithms, such as KCF, MCNN, SiamRPN, DaSiamRPN, and SlamRPN++, were selected for comparison with the proposed ILSN. The performance variations of the five algorithms on the UAV123 dataset are shown in Figure 5. The tracking success rate of the algorithm in this paper is 87.8%, and the tracking precision is 86.3% by taking a localization error threshold of 20 pixels, which is higher than that of other algorithms. For further verification of the tracking capability of the algorithm in complex scenes, tracking experiments in four complex scenes provided by the UAV123 dataset are described in this paper. The complex environment includes background interference, partial occlusion, out-of-field, and similar object interference. The experimental results are shown in Figure 6. Except for the three scenes of complete occlusion, background interference and out-of-field, the ILSN is tied for first place with the DaSiamRPN algorithm. The other three scenes are all ahead of the other algorithms by some margin. These indicate that the algorithm still has good tracking accuracy in complex scenes. Accuracy, robustness, and EAO were used as evaluation metrics. Accuracy measures the size of the overlap area between the predicted frame and the real frame. Robustness is related to the number of times the algorithm fails to track the target. EAO represents the evaluation of the predicted frame during the whole tracking process. Experiments were carried out to test the tracking speed of the algorithm.