## 5 Conclusions  
In this work, for the problem of poor small target detection, high false detection rate and high leakage rate in UAV scenarios, we propose a lightweight detection algorithm model LW-YOLO v8 based on the YOLO v8n model. Firstly, we add the channel attention mechanism SE module to this model to adaptively learn the importance of each feature channel to enhance the attention to the small target objects and to fully capture the global information, to adapts the problem of feature inconspicuousness of small targets in UAV scenarios; secondly, the use of lightweight convolution GSConv module instead of ordinary convolution maximally preserves the hidden connections between each channel, thus \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Networks** & **Pedestrian** & **People** & **Car** & **Van** & **Truck** & **Tricycle** & **Bus** & **Motor** & **[EMAIL_ADDRESS]\%** \\ \hline DetNet59 [21] & 15.3 & 4.1 & 36.1 & 17.3 & 20.9 & 13.5 & 26 & 10.9 & 15.3 \\ CornerNet [22] & 20.4 & 6.6 & 40.9 & 20.2 & 20.5 & 14 & 24.4 & 12.1 & 17.4 \\ Fast R-CNN [23] & 21.4 & 15.6 & 51.7 & 29.5 & 19 & 13.1 & 31.4 & 20.7 & 21.7 \\ CenterNet [24] & 22.6 & 20.6 & 59.7 & 24 & 21.3 & 20.1 & 37.9 & 23.7 & 26.2 \\ Mixed YOLO v3-LITE [25] & 34.5 & 23.4 & 70.8 & 31.3 & 21.9 & 15.3 & 40.9 & 32.7 & 28.5 \\ YOLO v8n & 33.5 & 27.3 & 75.5 & 37.9 & 27.7 & 21.6 & 45 & 35.5 & 32.5 \\ LW-YOLO v8 & 39.3 & 31.9 & 78.5 & 42.1 & 30.8 & 23.8 & 50.6 & 41.5 & 36.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detection results of different algorithms on VisDrone2019 dataset. Figure 6: Precision-Recall curve. (**a**) PR plot for target detection using YOLO v8n baseline model. (**b**) PR curves for target detection using the improved LW-YOLO v8 model. enriching the semantic information of the context, and designing a kind of one-shot aggregation module VoV-GSCSCP module to reduce the complexity of the computation and the network structure, and to reduce the amount of model computation while improving the detection accuracy. The experimental results on the UAV dataset VisDrone2019 show that the LW-YOLO v8 model proposed in this work outperforms other mainstream models for small target detection, [EMAIL_ADDRESS] reaching 36.3%, and the model's computational volume is 7.2 GFLOPs, which can effectively complete the task of detecting and recognizing small targets in aerial images from UAV, and in the future it has a positive role to promote in the fields of security monitoring, emergency rescue, agriculture and environmental detection, logistics and warehouse management, construction and site monitoring. Although the LW-YOLO v8 model is better than the baseline model in target detection in UAV aerial images, it still needs to be improved and optimized. For example, the accuracy and speed of target detection are still to be improved, and it has not yet met the requirements of practical applications, and we will carry out more in-depth research in the areas of model volume, detection speed, and number of parameters to contribute to the development of target detection in the field of UAV aerial images in the future. We will perform more in-depth research on model volume, detection speed, number of parameters, etc. In order to contribute to the future development of target detection in UAV aerial images. Z.H. : Conceptualization, Methodology, Resources, and Writing--review and editing, formal analysis, Validation and reviewing. S.L. : Investigation, Data collection, Data interpretation, data curation, Software, Validation and Visualization, formal analysis, and Writing--original draft. All authors have read and agreed to the published version of the manuscript. This work was supported by the Funds of key Research Project of Higher Education Institutions in Henan Province, China (No.21A520026), and the Funds of Science and Technology Research Projects in Henan Province, China (No.202102210378). Not applicable. The public data presented in this study are available on request from the corresponding author.