# Research on Vehicle Re-Identification Algorithm Based on Fusion Attention Method
## 3 Experiments and Discussion
### Experimental Results and Analysis
#### 3.4.2 Experimental Results and Analysis of VehicleID Dataset  
For VehicleID dataset, three different scales will be used for testing, namely 800, 1600, and 2400, according to the protocol of the dataset. The VehicleID dataset is tested using mean precision (mAP) and cumulative matching feature (CMC) to measure the model's performance obtained from the experiments, where higher values of mAP and CMC indicate re-identification of the same vehicle the higher accuracy. The results of the ablation experiments on the VehicleID dataset are shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**VehicleID** & & **ResNet-Baseline** & **+Ret+Tri** & **+Ret+Tri+AuL3** & **+Ret+Tri+AuL2** & **SDLAU** \\ \hline \multirow{4}{*}{Small (800)} & mAP & 71.99\% & 74.01\% & 81.73\% & 79.12\% & 82.87\% \\  & Rank-1 & 88.53\% & 91.89\% & 95.73\% & 95.12\% & 95.89\% \\  & Rank-5 & 93.35\% & 95.33\% & 97.59\% & 97.44\% & 97.79\% \\  & Rank-10 & 96.63\% & 97.38\% & 98.31\% & 97.96\% & 98.38\% \\ \hline \multirow{4}{*}{Medium (1600)} & mAP & 71.19\% & 73.92\% & 78.54\% & 76.08\% & 80.33\% \\  & Rank-1 & 87.59\% & 90.69\% & 94.83\% & 94.47\% & 95.56\% \\ \cline{1-1}  & Rank-5 & 91.63\% & 94.11\% & 96.75\% & 96.67\% & 96.98\% \\ \cline{1-1}  & Rank-10 & 93.52\% & 96.12\% & 97.35\% & 97.25\% & 97.71\% \\ \hline \multirow{4}{*}{Large (2400)} & mAP & 68.58\% & 70.39\% & 76.38\% & 74.85\% & 77.86\% \\ \cline{1-1}  & Rank-1 & 84.69\% & 89.97\% & 94.61\% & 94.06\% & 94.85\% \\ \cline{1-1}  & Rank-5 & 90.61\% & 93.01\% & 95.98\% & 95.71\% & 96.83\% \\ \cline{1-1}  & Rank-10 & 91.97\% & 95.92\% & 97.02\% & 96.46\% & 97.63\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study results on VehicleID dataset. Figure 10: Visualization results for the ranking list of VeRi-776. From the table, it can be seen through step-by-step ablation experiments that the mAP and rank values obtained after applying the data augmentation technique (Ret) and ternary loss function (Tri), combined with shallow layer-2 features (Shallow layer-2) and deep layer-3 features (Deep layer-3) to the basic ResNet-Baseline depth network, are significantly higher. Ret is the abbreviation of data augmentation technique, Tri denotes the triadic loss function, and AuL3 and AuL2 denote the vehicle features extracted directly in ResNet-Baseline from Layer-3 and Layer-2 are added to the Attention unit. For the VehicleID dataset, the Small (800) dataset is used as an example to analyze the experiment. Through the base network ResNet-Baseline by image preprocessing data enhancement technique, the joint training of cross-entropy loss function and triadic loss function has a certain improvement in the accuracy of vehicle re-identification. mAP for the base network ResNet-baseline is improved from 71.99% to 74.01%, and Rank-1, Rank-5, and Rank-10 are improved by 3.36%, 1.98%, and 0.75%, respectively, compared with the baseline model, and the experimental results of adding the attention mechanism ResNet-Baseline are also better improved. The final vehicle re-recognition obtained by fusing the attention mechanism on the shallow layer (Shallow layer-2) and deep layer (Deep layer-3) of the base model (ResNet-SDLAU) improves mAP, Rank-1, Rank-5, and Rank-10 by 10.88%, 7.36%, 2.44%, and 1.75%, respectively, compared with the baseline model. The results show that the vehicle re-identification model (ResNet-SDLAU) proposed in this paper can significantly improve the performance of the model. Table 4 shows the comparison results of three test datasets of different sizes. It can be observed that the data augmentation technique and triple loss function, combined with shallow features (Shallow layer-2), deep features (Deep layer-3), and attention mechanism network, are applied to the basic ResNet-Baseline deep network. The model accuracy has significantly improved in the vehicle re-identification model ResNet-SDLAU. This is because some key vehicle regions are further learned by adding the attention mechanism method.