# A Lightweight Uav Swarm Detection Method Integrated Attention Mechanism
## 4 Experimental Results and Analysis
### Comparison Experiment  
In order to objectively reflect the performance of the UAV swarm object detection network proposed in this paper, this study also uses the same settings to train other lightweight YOLO models and conducts a comparative analysis. The comparison results are shown in Table 6. It can be seen that under the same test set, the mAP value of the proposed UAV detection model has reached 82.32%, which is 15.59%, 15.41%, 1.78%, 0.58%, and 1.82% higher than MobileNet3-yolov4, GhostNet-YOLOv4, YOLOv4-Tiny, YOLOX-Tiny, and YOLOX-Nano, respectively. At the same time, the total network parameters and model size are optimal. The experimental results that are obtained on the computational time are tabulated in Table 6. It should be said that, due to limitations in the hardware platform, our experiment did not achieve the effect in the original paper, but we believe that the proposed method will have lower latency and higher processing speed under the condition of higher computing power. Table 6 illustrates that, compared with the baseline model YOLOX-Nano, the proposed model achieves higher recognition accuracy under approximately the same inference \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **[EMAIL_ADDRESS] (\%)** & **Params (\(\times 10^{6}\))** & **Model Size/Mb** & **Latency/ms** & **FPS** \\ \hline GhostNet-YOLOv4 & 66.91 & 11.00 & 42.40 & 83 & 12 \\ MobileNet3-YOLOv4 & 66.73 & 11.30 & 53.70 & 90 & 11 \\ YOLOv4-Tiny & 80.54 & 5.87 & 22.40 & 45 & 22 \\ YOLOX-Tiny & 81.74 & 5.03 & 13.70 & 55 & 18 \\ YOLOX-Nano & 80.50 & 0.89 & 3.70 & 71 & 14 \\ Ours & 82.32 & 0.93 & 3.85 & 71 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison experiment of different networks. Figure 13: Precision and Recall of UAV detection. time. Thus, our model can also meet the requirements of being real-time and is suitable for applications that require low latency. However, the YOLOX-Tiny and YOLOv4-Tiny networks are quicker than the proposed model, as they run 18 FPS and 22 FPS, respectively. Even though the proposed architecture is slower than these two models, significantly, it provides higher detection accuracy. Therefore, the proposed model is a lightweight model with high detection accuracy and suitable for various edge computing devices. In order to verify the detection effect of the UAV detection model proposed in this paper in actual scenes, some images in the dataset are selected for detection, and the comparison diagram of the detection effect is shown in Figure 14. As shown in Figure 14, in an environment with a simple background, most models can successfully identify UAVs. The proposed method can detect more small and distant objects than the baseline model YOLOX-Nano. As shown in Figure 14, when the background is complex or UAVs are densely distributed, there are many undetected phenomena with the other models. The algorithm in this paper shows better detection performance and can accurately detect UAVs when occlusion occurs among objects. As shown in Figure 14, when the image resolution is low, the algorithm in this paper can still accurately detect UAVs with complex backgrounds and provide higher confidence scores. Through comparison, it can be demonstrated that our method has improved detection accuracy and confidence, which shows that it can satisfy the requirements of being lightweight and providing higher accuracy, meeting the requirements of industrial applications. Figure 14: Comparison of detection results of different models. In addition to the construction of an anti-UAV system, the algorithm proposed in this paper can also be applied as a swarm intelligence algorithm to achieve UAV swarm formation, multi-UAV cooperation, etc. By deploying lightweight object detection methods, UAVs in the swarm can quickly and accurately obtain the position and status of other partners, so that they can adjust themselves in time according to the swarm intelligence algorithm.