# A Efficient and Accurate UAV Detection Method Based on YOLOv5s
## 4 Results
### Experimental Results Analysis  
Based on the experimental results in Table 3, it is observed that using ShufflenetV2 as the backbone network on a custom small target dataset shows higher GFLOPs and FPS compared to Ghost-Net, ShuffleNetV1, and MobileNetV2. The GFLOPs and FPS for ShufflenetV2 are 2.3 and 186, respectively. Despite a slight decrease in precision and recall, with precision and recall rates of 91.2% and 86.3%, respectively, ShufflenetV2 demonstrates significant advantages. Specifically, compared to MobileNetV2, although there is a slight decrease in precision and recall, the GFLOPs decreased from 2.8 to 2.3, while the detection speed increased from 183 to 186 FPS. This indicates that ShufflenetV2 has significant advantages in performance and speed when handling small target detection tasks. Therefore, ShufflenetV2 is a promising and efficient choice as the backbone network for small target detection tasks. The experimental results in Table 4 indicate that using Bi-FPN as the neck network results in outstanding performance across metrics such as mean average precision, recall rate, precision, floating-point operations, and detection speed. In tasks involving small targets like drones and birds, Bi-FPN outperforms PANet in performance. Bi-FPN enriches the model's feature extraction information as the neck network, effectively enhancing the neural network's capability to detect small targets, thereby improving the model's accuracy and efficiency. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **mAP/\%** & **R/\%** & **P/\%** & **GFLOPs** & **FPS S\({}^{-1}\)** \\ \hline YOLOv5s-SV2-P & 91.1 & 86.3 & 91.2 & 2.3 & 186 \\ YOLOv5s-SV2-B & 91.4 & 87.4 & 91.8 & 2.2 & 187 \\ \hline \hline \end{tabular} \({}^{2}\) SV2 = ShufflenetV2; P = PANet; B = BiFPN.  
\end{table} 
Table 4: Comparison of experimental results of neck network. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Related Parameters** \\ \hline GPU & NVIDIA GeForce GTX 4070 \\ CPU & i9-14900 KF/32G \\ GPU Memory & 12 GB \\ Operating System & Ubuntu 20.04 \\ Computing Platform & Cuda 12.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hardware platform configuration. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **mAP/\%** & **R/\%** & **P/\%** & **GFLOPs** & **FPS S\({}^{-1}\)** \\ \hline YOLOv5s & 93.9 & 90.2 & 93.0 & 16.0 & 153 \\ YOLOv5s-G & 91.7 & 88.7 & 92.4 & 5.8 & 178 \\ YOLOv5s-SV1 & 92.2 & 88.5 & 92.5 & 3.6 & 177 \\ YOLOv5s-MV2 & 92.5 & 87.1 & 92.6 & 2.8 & 183 \\ YOLOv5s-SV2 & 91.1 & 86.3 & 91.2 & 2.3 & 186 \\ \hline \hline \end{tabular} \({}^{\dagger}\) G = Ghost-Net; SV1 = ShuffleNetV1; MV2 = MobileNetV2; SV2 = ShufflenetV2.  
\end{table} 
Table 3: Experimental performance of backbone network Comparison. The proposed algorithm was tested through ablation experiments on a custom-built dataset, and the results validated its effective detection performance, illustrated in Figure 8 as a graphical representation of the merged datasets. This study contributes to the real-time and efficient detection of UAVs, facilitating the establishment of early warning systems and enhancing security measures for high-value targets. The ablation study results presented in Tables 3 and 5 demonstrate that replacing the backbone network of YOLOv5s with ShufflenetV2 significantly reduces computational complexity. Additionally, replacing some convolutional modules with Ghost Conv modules has notably decreased GFLOPs while maintaining a high detection speed. GFLOPs dropped from 16 to 2.1, with only a 3% decrease in mAP. By implementing the Bi-FPN, the network's feature fusion capabilities were further enhanced, leading to a 1% increase in detection accuracy. The introduction of the CA attention mechanism strengthened feature extraction capabilities, thereby improving detection precision, which only saw a 1% decrease compared to the original YOLOv5s. Transitioning from the CIoU loss function to the EIoU loss function significantly enhanced recall rates and mAP.