# A Lightweight Small Target Detection Algorithm for UAV Platforms
## 4 Experiments and Results
### Ablation Experiments  
To verify the effectiveness of the neck improvement on the model's weight, YOLOv8n is used as the baseline for comparison with the lightweight model after the addition of the small target detection layer and the removal of the large target detection layer. The experimental results are shown in Table 1, which indicates that the accuracy of the model is still improved even when the number of parameters is reduced by one-third, achieving the desired goal of lightweighting. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Module** & **Dataset** & **Parameters (M)** & **GFLOPs** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS] (\%)** \\ \hline Baseline & 3.0 & 8.1 & 32.1 & 18.6 \\ Lightweight & VisDrone2019 & 2.0 & 11.5 & 34.9 & 20.7 \\ \hline Baseline & 3.0 & 8.1 & 14.0 & 5.5 \\ Lightweight & TinyPerson & 2.0 & 11.5 & 18.9 & 7.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Lightweight improvement experiment. In the improvement of the SPD structure, the impacts of different pooling methods on the detection effect were explored, including using average pooling alone, max pooling alone, and applying both average pooling and max pooling simultaneously. The experimental results are shown in Table 2.  
It can be seen from the experimental results that the original SPD module has improved the detection accuracy of the model, but the improvement is rather slight. During the improvement of SPD, using average pooling or max pooling alone can further improve the detection accuracy, yet the effect is not as good as the improvement method of using both average pooling and max pooling simultaneously. It has been proved that the CAMSPD module formed by splicing the features of average pooling and max pooling has the best performance. In order to investigate the effect of the three modules, RFCAConv, CAMSPD, and C2 GELAN, on network performance, the lightweight model is first obtained through the improvement of the neck. Based on this model, ablation experiments are carried out on the three modules, respectively, and the experimental results are shown in Tables 3 and 4. The symbol \"\(\check{\textbf{\emph{\prime}}}\)\" indicates that this improved method is added to the lightweight model. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn  
From the experimental results, it can be seen that several improvements have a relatively positive effect on the comprehensive performance of the model, with an increase only in the amount of computation:  
(1) In the experiments using the three modules alone, each module improves the accuracy index of the model, with only a slight increase in the number of parameters and the amount of computation, while the CAMSPD module brings about the largest increase in both the number of parameters and the amount of computation. (2) In the comprehensive improvement of each module, since the C2 GELAN module uses group convolution at the input, the introduction of this module effectively reduces the increase in the number of parameters brought about by the CAMSPD module. Compared with the individual action of each module, the indicators of the comprehensive improvement are higher than those of the individual action, and the performance enhancement is obvious. (3) The optimal experimental results are achieved by adding these three modules to the network at the same time. Compared with the baseline model, not only is the number of parameters reduced by 23.3%, but also P, R, [EMAIL_ADDRESS], and [EMAIL_ADDRESS]:0.95 are improved by 6.9%, 7.2%, 8.4%, and 5.8%, respectively. This achieves the comprehensive optimal effect and provides better performance for small target detection tasks under the UAV viewpoint. (4) Regarding the balance between detection accuracy and detection speed, whether with the VisDrone2019 dataset or the TinyPerson dataset, all three modules have brought about a decrease in detection speed while improving the detection accuracy.