# 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  
In this section, a series of comparative experiments are conducted on the VeRi-776 dataset to verify the effectiveness of the algorithmic models proposed in this paper. Four models are tested for experimental comparison as:  
1. The base model without using the attention mechanism (ResNet-baseline);
2. The ResNet-layer3 model using the attention mechanism fused in the deep layer-3 layer;
3. The ResNet-layer2 model using the attention mechanism fused in the shallow layer-2 layer;
4. ResNet-SDLAU model proposed in this paper. By analyzing the experimental results of the cumulative matching curve CMC, the average accurate precision mAP and the loss function loss curve of the training process of the VeRi-776 data set training process and the CMC curves Rank-1, Rank-5, and Rank-10 of the proposed method are shown in Figure 6. It can be observed that with the deepening of network training iterations, the rank indicator has been increasing, which proves the effectiveness of the model. The loss curve is shown in Figure 7. After the model is established and the parameters are determined, the ResNet-SDLAU model can be trained. The figure below shows the monitoring of the model training process. It can be seen that the loss decreases during the training process. In the first 10 stages of training, the model loss value drops rapidly; from 10 to 60 epoch, the model gradually stabilizes. The effectiveness of the joint training of cross-entropy loss function and triplet loss function is proved. The mAP curve of the model training process is shown in Figure 8. The ResNet-SDLAU model in this paper is used to predict the target classification of each image in the test set, and the accuracy rate is used to measure the model performance. After every five epochs of training, the accuracy of the prediction results of every five epochs is recorded. Statistics show that the network trained in this chapter can predict the recognition of the same vehicle with high precision, so the comprehensive performance of this model is good. The ablation experimental result is shown in Table 1.  
In Table 1, Ret is the abbreviation of image enhancement technology (Retinex), Tri is the abbreviation of the triple loss function (Triple), and AuL means that the vehicle features  
Figure 8: VeRi-776 mAP curve. Figure 6: CMC curve of VeRi-776 dataset. Figure 7: VeRi-776 loss curve. of the layer, Layer, extracted directly in the ResNet-baseline, are added to the attention mechanism unit (attention unit); AuL3 and AuL2 indicate that the vehicle features of the third layer, Layer-3, and the second layer, Layer-2, extracted directly from the ResNet-baseline, are added to the attention unit ratio. From Table 1, it can be observed that the preprocessing technique and triple loss function can effectively improve the model's performance. For example, these two training strategies can improve the mAP and Rank-1 of the ResNet baseline by 4.22% and 3.11%, respectively. When adding attention to the Layer-3 layer of ResNet, the mAP of our model improves from 69.60% (ResNet) to 71.32% (ResNet-AuL3). It can also be observed that combining the feature maps (Layer3) and (Layer2) of different layers extracted by ResNet can also improve the performance of the model. By further extracting feature maps, the model mAP is improved from 74.50% to 78.30% (SDLAU), which is a good improvement. After comparing with the model benchmark, the complex background information of the vehicle is preprocessed by image enhancement technology. Then, the cross-entropy and triplet loss functions are used to jointly train on the basic model Shallow layer-2 and deep (Deep layer-3). The final vehicle re-identification model (ResNet-SDLAU) obtained by integrating the attention mechanism has mAP, Rank-1, Rank-5, and Rank-10, which are improved by 8.7% and 11.24%, respectively, compared with the baseline model 7.32% and 5.71%.