## 3 Results
### Experiment 1: Fusion Configuration Research  
Firstly, we evaluate the performance of the transformer encoder fuse with the detection framework. We select five pre-fusion positions in the baseline detection networks shown in Figure 6; we add the transformer encoder, train, and test different configuration module's  
Figure 8: The annotation information of a labeled ship chip.  
performance separately according to the corresponding relationship. Hyperparameter settings: SGD optimizer with 0.01 learning rate; the batch size is set to 16. the number of workers is set to 8; After 300 epochs, Table 1 listed the results.  
We count the power consumption of the GPU in the calculation process. Figure 9 plots the \(mAP\)-Epoch curve and GPU power usage-training time curve. We can see that different configurations also correspond to different training times and power, fusing the transformer module in the neck position can achieve higher performance gains, as well as relatively low training power consumption.  
By comparing the training time and power consumption of the baseline and the proposed method, it can be seen that adding the transformer module increases the computational complexity. In addition, by observing the experimental data, adding a transformer module on Position 1 and Position 2 significantly results in longer training time and higher training power consumption than adding a transformer module on Position 3, Position 4, and Position 5, which is due to the matching for the multiple layers of the backbone network, the transformer encoder with a corresponding multiple, as shown in Figure 6. Furthermore, the front part of the network has a higher resolution feature map than the end of the network. Thus, fusing the transformer encoder in the backbone brings higher computational complexity than fusing the transformer encoder in the neck.  
Interestingly, although the computational cost of integrating the transformer at the end of the network is not high, it brings a significant improvement in \(mAP\). We can find that by adding the transformer module in Position 4 and Position 5, the model performance  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & \(mAP_{0.5}\) & \(mAP_{0.50.95}\) & **Training** & **Consumption (W)** & **FLOPs (G)** \\  & & & (\%) & (\%) & Time(min) & **Consumption (W)** & **FLOPs (G)** \\ \hline YOLOv5 + ViT(1) & 96.88 & 91.94 & 96.49 & 60.95 & 73.1 & 172.9 & 1.1 \\ YOLOv5 + ViT(2) & 95.50 & 93.04 & 96.60 & 60.03 & 73.1 & 171.6 & 1.1 \\ YOLOv5 + ViT(3) & 97.60 & 91.00 & 96.73 & 60.59 & 55.2 & 161.5 & 1.1 \\ YOLOv5 + ViT(4) & 94.71 & 91.58 & 96.26 & 62.04 & 55.6 & 155.7 & 1.1 \\ YOLOv5 + ViT(5) & 98.76 & 90.84 & 96.60 & 61.52 & 46.7 & 151.9 & 1.1 \\ YOLOv5 + ViT(1+2) & 95.59 & 91.58 & 96.77 & 60.20 & 73.6 & 172.9 & 1.1 \\ YOLOv5 + ViT(3++5) & 95.52 & 91.21 & 96.17 & 58.80 & 64.8 & 162.1 & 1.1 \\ Baseline & 95.28 & 92.67 & 96.62 & 61.38 & 45.9 & 148.7 & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment results of different fusion configurations.  
Figure 9: The **upper-left** subgraph plots the Epoch-\(mAP\) curve; the **upper-right** subgraph plots the Epoch–\(mAP\) curve; the **lower-left** subgraph is the best \(mAP\) in 300 epochs; the **lower-right** subgraph plots the GPU power usage and training time curve.  
metrics have been improved. In other words, fusing the transformer module at the end of the network to capture more \"global\" context information in the low-resolution feature map, is more efficient for improving performance.