# A Lightweight Small Target Detection Algorithm for UAV Platforms
## 4 Experiments and Results
### Comparison Experiments with Advanced Models  
In order to verify the superiority of the improved model in this paper, the improved model is compared with mainstream detection models such as YOLOv5s, YOLOv6s [26], YOLOv8s, YOLOv9c, and other representative network models, as well as with other improved YOLO models. The experimental results are shown in Tables 5 and 6. By analyzing the experimental results, it can be proved that the model proposed in this paper has significant advantages:  
Figure 10: These are the detection effects in the actual shooting scenes of UAVs. These two pictures do not belong to the VisDrone2019 dataset or the TinyPerson dataset. (1) YOLOv9c achieves the optimal values for all the detection performance indicators, but the number of parameters and the computation amount are huge, which is not suitable for low-computing-power devices of UAV platforms. (2) YOLOv5s, which is also lightweight, has a lower computational volume than the improved model, but its performance indexes are not as good as the improved model when the number of parameters is 0.2 M higher, which cannot satisfy the requirement of detection accuracy for UAV visual inspection tasks. (3) Compared with the lightweight improved model, FFCA-YOLO, from the same UAV perspective, only the recall rate R of the improved model in this paper is lower than that of FFCA-YOLO, while the rest of the indexes are all better than those of FFCA-YOLO, indicating a better comprehensive effect. Figure 11 shows the curve diagrams of index changes during the training process of each model in the comparative experiments and the improved model in this paper. From the training results, it can be seen that the model in this paper generally performs better than advanced detection models, demonstrating its superiority in the field of target detection, especially in small target detection scenarios on UAV low-computing-power platforms, showcasing high efficiency and reliability. This is also shown in Figure 12, for the visualization of the advanced model and the improved model in this paper. In Scene 1, the numbers of detection targets for each model are 76, 78, 108, 102, and 104, in sequence from top to bottom as shown in the figure. In Scene 2, they are 73, 71, 101, 91, and 89. And in Scene 3, they are 78, 80, 114, 98, and 106. It can be seen from the detection results that YOLOv9c has achieved the best detection accuracy. However, compared with the improved model in this paper, YOLOv9c has a huge number of parameters and computational complexity, while only having a slight advantage in accuracy. Specifically, on the two datasets, the [EMAIL_ADDRESS] of YOLOv9c is 2.5%, which is 1.3% higher than that of RSG-YOLO, respectively, but its number of parameters is 23 million higher and its GFLOPs is 83.4 higher. Moreover, its detection speed is also inferior to that of the improved model in this paper. Overall, its performance on the low-computing-power platform of \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Module**} & **Parameters** & \multirow{2}{*}{**GFLOPs**} & \multirow{2}{*}{**P (\%)**} & \multirow{2}{*}{**R (\%)**} & \multirow{2}{*}{**[EMAIL_ADDRESS] (\%)**} & \multicolumn{2}{c}{**[EMAIL_ADDRESS]:0.95**} & \multirow{2}{*}{**FPS**} \\  & **(M)** & & & & & & \\ \hline YOLOv5s & 2.5 & 7.1 & 30.5 & 16.2 & 11.1 & 4.1 & 108.6 \\ YOLOv6s & 4.2 & 11.8 & 24.6 & 14.6 & 10.2 & 3.7 & 120.9 \\ YOLOv8n & 3.0 & 8.1 & 30.4 & 16.8 & 14.0 & 5.5 & 106.6 \\ YOLOv8s & 11.1 & 25.8 & 35.9 & 20.5 & 20.1 & 8.7 & 100.1 \\ YOLOv9c & 25.3 & 102.4 & 38.2 & 26.3 & 24.6 & 10.4 & 86.2 \\ FFCA-YOLO & 5.1 & 37.3 & 35.5 & 26.0 & 23.2 & 8.8 & 111.7 \\ Ours & 2.3 & 19.0 & 36.1 & 25.8 & 23.3 & 9.1 & 98.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The comparison experiment results on the TinyPerson dataset.