## 5 Conclusions  
This paper uses LB to enhance shallow detail features and CLAU to achieve pixel-level adaptive fusion of detail and semantic features. Both modules show improved network performance in small target detection to varying degrees. Further, we combine the two  
Figure 10: Comparison of effects of LB and CLAU on YOLOv5s and YOLOv5n.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Backbone** & **Parameters** & **Precision** & **Recall** & \multicolumn{2}{c}{**AP@5 (\%)**} & \multirow{2}{*}{**[EMAIL_ADDRESS] (\%)**} \\ \cline{3-3} \cline{5-7} YOLOv3 & Darknet53 & **(M)** & **(\%)** & & **Car** & **Airplane** & \\ \hline RetinaNet & ResNet-101-FPN & - & - & - & 84.64 & 90.51 & 87.57 \\ Faster R-CNN & ResNet-101 & 44.55 & - & - & 86.87 & 89.86 & 88.36 \\ RoI Trans & ResNet-101-FPN & - & - & - & 87.99 & 89.90 & 88.95 \\ DAL & ResNet-101 & 44.55 & - & - & 89.25 & 90.49 & 89.87 \\ Oriented RepPoints & ResNet-101-FPN & - & - & - & 89.51 & 90.70 & 90.11 \\ \hline YOLOv5n & - & 1.77 & 92.46 & 85.96 & 86.64 & 96.09 & 91.36 \\ YOLOv5s & - & 7.06 & 93.5 & 86.43 & 87.16 & 96.15 & 91.66 \\ Tiny LC-YOLO & - & **1.83** & 94.28 & **89.31** & 90.84 & 97.51 & **94.17** \\ YOLOv5l & - & 46.61 & 94.73 & 87.65 & **91.86** & 96.51 & 94.19 \\ YOLOv5m & - & 21.04 & 94.98 & 89.95 & 90.91 & 97.56 & 94.24 \\ LC-YOLO & - & **7.30** & 94.76 & **90.83** & **91.82** & **98.10** & **94.96** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of different models on the UCAS-AOD dataset.  
modules to obtain the LC-YOLO model. With only a few parameters added, the model efficiently uses the extracted features to realize high-precision detection of small targets. Experiments on the UCAS-AOD dataset show that our lightweight LC-YOLO network has advantages in small target detection tasks. Its mAP is 3.30% better than that of the YOLOv5s network, surpassing the heavy networks and advanced target detection models widely used in many projects, and even exceeding heavier networks such YOLOv5m and YOLOv5l under the same framework. In small target detection tasks, LC-YOLO and Tiny LC-YOLO demonstrate exceptional detection performance with minimal parameters and memory consumption while exhibiting strong target retrieval capabilities. This is advantageous for deploying and utilizing small target detection networks on mobile edge chips in practical projects.  
Conceptualization, M.C.; methodology, M.C.; software, M.J.; validation, M.C., H.W. and G.G.; formal analysis, M.C.; investigation, M.C.; resources, G.C. and H.L.; data curation, M.C.; writing--original draft preparation, M.C. and H.W.; writing--review and editing, G.G.; visualization, M.C.; supervision, W.M.; project administration, G.C. and H.L.; funding acquisition, M.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (U19A2080) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA27040303, XDA18040400, XDB4400000).  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
The authors would like to thank the creators of the open access UCAS-AOD remote sensing image dataset.  
The authors declare no conflict of interest.