# Research on Vehicle Re-Identification Algorithm Based on Fusion Attention Method
## 3 Experiments and Discussion
### Experimental Results and Analysis
#### 3.4.1 Experimental Results and Analysis of VeRi-776 Dataset
The results show that the vehicle re-identification model (ResNet-SDLAU) proposed in this paper can significantly improve the model's performance. The text continues here (Figure 2 and Table 2). We also compared the accuracy of the constructed vehicle re-identification model with the models that have had better performance in recent years, and the results are shown in Table 2. According to the comparison results, the model proposed in this paper achieves higher mAP than most other models. Among them, the mAP obtained by Siamese+Path [19], VAMI [9], RAM [20], OIFE [21], AAVER [22], VANet [23], PAMTRI [7], and PRN [24] are 58.3%, 50.1%, 61.5%, 61.2%, 66.3%, 71.9%, and 74.3%, respectively, which are lower than the vehicle re-identification model (ResNet-SDLAU) proposed in this paper by 78.30%. Among them, Siamese+Path uses a single-angle feature map to generate vehicles with multiple angles, but it is significantly different from the actual features and the model's average performance. RAM is a model with multiple branches that can extract more discriminative features of vehicles. However, the model data is prone to overfitting during model training. AAVER is a two-way adaptive attention model. One path is used to obtain the global characteristics of the vehicle, and the other is to obtain local characteristics by focusing on the key points with the largest amount of information. However, this method does not consider the vehicle's attributes and other related information. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **mAP** & **Rank-1** & **Rank-5** & **Rank-10** \\ \hline ResNet-baseline & 69.60\% & 85.12\% & 91.20\% & 93.63\% \\ ResNet-baseline+Ret & 71.90\% & 86.20\% & 95.20\% & 96.83\% \\ ResNet-baseline+Ret+Tri & 73.82\% & 88.23\% & 96.83\% & 97.23\% \\ \hline ResNet-baseline+AuL3 & 71.32\% & 87.46\% & 92.13\% & 94.35\% \\ ResNet-baseline+AuL3+Ret & 72.36\% & 89.94\% & 95.89\% & 97.23\% \\ ResNet-baseline+AuL3+Ret+Tri & 74.50\% & 91.23\% & 97.49\% & 98.17\% \\ \hline ResNet-baseline+AuL2 & 70.58\% & 86.74\% & 91.57\% & 93.87\% \\ ResNet-baseline+AuL2+Ret & 71.56\% & 88.65\% & 94.97\% & 96.43\% \\ ResNet-baseline+AuL2+Ret+Tri & 73.86\% & 90.73\% & 96.80\% & 97.37\% \\ \hline ResNet-baseline+AuL3+AuL2 & 74.77\% & 89.25\% & 96.50\% & 97.38\% \\ ResNet-baseline+AuL3+AuL2+Ret & 75.35\% & 93.59\% & 98.21\% & 99.05\% \\ ResNet-baseline+AuL3+AuL2+Ret+Tri & 78.30\% & 96.36\% & 98.51\% & 99.34\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation study results on the VeRi-776 dataset. The results show that the models (RAM, AAVER, VANet) using both global and local features achieve better results than those using only global features. PRN uses the detection network to locate the part area, and the mAP of this model is 74.3%. Our model implicitly localizes discriminative regions without using any partial annotations, achieving a mAP of 78.30%, which is the best, demonstrating the effectiveness of the performance of the fused attention model. A set of attention visualizations were performed on the final output feature maps of Baseline (ResNet-baseline), Baseline (ResNet-baseline)+L2, Baseline (ResNet-baseline)+L3, and ResNet-SDLAU, as shown in Figure 9. It can be noted that the feature maps of the baseline pay little attention. (ResNet-baseline) just pays less attention to some vehicle regions. As shown in columns 2 and 3, using (ResNet-baseline) +L2 and +L3, different attention mechanisms focus on some salient features instead of some useless areas. The attention of ResNet-SDLAU proposed in this paper can achieve a better balance because it focuses on the salient local parts of the vehicle, such as distinguishable local features such as logos, lights, windows, wheels, etc. This means that the proposed model is better suited to the nuanced features of the vehicle, and the feature embedding becomes more decorrelated and diverse depending on the mode of attention mechanism used.