## 4 Experiments
### Ablation Experiments  
We performed ablation experiments on the UCAS-AOD dataset to verify the effectiveness of our LB and CLAU modules. The output feature maps of each network layer were visualized, with the results shown in Figures 7 and 8. When detecting vehicle and aircraft samples, both the LB module and CLAU module demonstrate improvements in network performance. According to the optimization scheme presented in Figure 5, when only the LB module was used to optimize the basic network it was able to enhance the detailed features extracted from the shallow layer of the network. Specifically, the second line of the feature maps shows that the contour of the small targets in the second and fourth layer of the network feature map is enhanced (indicated by brighter colors). On the other hand, with only the CLAU module used to optimize the baseline, the location information of small targets appears in the eleventh and fifteenth layer network outputs of the third \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Relative Scale 1** & **Absolute Scale 2** \\ \hline Car & 84.68\% & 56.18\% \\ Airplane & 53.23\% & 20.14\% \\ \hline \hline \end{tabular}  
* The proportion of number of samples conforming to the definition of “relative scale” in this category’s total number of instances. 2 The proportion of the number of samples conforming to the definition of relative scale in this category’s total number of instances.  
\end{table} 
Table 1: The size statistics of the target in the images of the UCAS-AOD dataset adjusted to 800 \(\times\) 800. Figure 6: Category distribution of small targets conforming to the definition in the UCAS-AOD dataset. line, indicating that the CLAU module supplements detailed information in the deep network. With both the LB module and CLAU module used for optimization simultaneously, the detailed features extracted by the shallow network are enhanced and the CLAU model supplements the deep network. The detection effect diagram confirms that the LC-YOLO model improves its missed detection rate for small targets. In version 6.0 of YOLOv5, YOLOv5n was introduced, which has the same network depth as YOLOv5s with half the network width. The features generated by each channel of the network make different contributions to the network prediction. With training schemes such as random gradient descent and cosine function learning rate adjustment, YOLOv5n effectively extracts features for network prediction with a smaller model size, resulting in slightly lower detection accuracy than YOLOv5s. Consequently, we optimized YOLOv5n with the same scheme to obtain a smaller version of LC-YOLO and conducted ablation experiments. Corresponding to the above visualization results, Table 2 show the Precision and Recall of our ablation experiments based on YOLOv5s and YOLOv5n. According to conventional understanding, Recall measures a model's ability to retrieve relevant information compared to the ground truth, while Precision reflects the proportion of retrieved prediction boxes that actually contain the correct target. While there is no inherent relationship between Precision and Recall (as can be seen from the formulas), they are often interdependent in practical applications. Achieving a balance between these metrics is therefore crucial. When using either the LB module or CLAU module to optimize YOLOv5s and YOLOv5n networks, the Recall rate of the network for vehicle samples improved significantly due to effective utilization of details. The combination of the two modules resulted in varying  
Figure 8: The feature visualization of part of the network layer in different model detection processes in the ablation experiment for airplane samples. Figure 7: The feature visualization of part of the network layer in different model detection processes in the ablation experiment for car samples. degrees of improvement in Accuracy and Recall for detecting both vehicle and aircraft samples, achieving a relatively good balance.