## 4 Experiments
### Ablation Experiments
When calculating performance metrics such as Precision, Recall, and mAP, the IOU threshold is commonly set to 0.5. This means that if the IOU value between the predicted box and the ground truth is greater than 0.5, the predicted box is considered a true positive (TP). Figure 9 illustrates that the overall confidence of the TP boxes predicted by the network is improved in the YOLOv5s model optimized by the LB and CLAU modules. Generally, the LC-YOLO model has the highest confidence for TP boxes. The overall improvement in confidence for the true positive (TP) samples indicates that the optimization scheme proposed in this paper enhances the network's ability to detect the target against complex backgrounds, as evidenced by the improved recall value. As shown in Table 3, after incorporating LB, the AP@5 of the car samples (which are smaller) was increased by 3.07% from 87.16% to 90.32% without any additional learnable parameters; furthermore, mAP@5 achieved a certain degree of improvement. When only CLAU was incorporated into the model, the AP of the car and airplane samples increased by 3.48% and 1.43%, respectively, with only a few parameters increased. It is worth pointing out that the AP of the smaller car samples improved more. At the same time, mAP saw a considerable improvement, from 91.66% to 94.11%. Combining LB and CLAU to obtain the lightweight LC-YOLO model resulted in a 4.66% improvement in the AP of the car samples and a 1.95% improvement in the AP of the plane samples; the mAP@5 was 94.96%, a significant increase of 3.3%. Table 4 presents the model parameters, forward reasoning time, and computation. It can be seen that the LB model does not increase the number of learnable parameters and the computation remains almost the same, while the reasoning time is increased due to data transfer between the CPU and GPU during the design of convolutional kernel parameters, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Precision (\%)**} & \multicolumn{2}{c}{**Recall (\%)**} \\ \cline{2-5}  & **Car** & **Airplane** & **Car** & **Airplane** \\ \hline YOLOv5s & 89.70 & 97.29 & 79.72 & 93.14 \\ YOLOv5s+LB & 87.57 & **98.60 (+1.31)** & **85.64 (+5.92)** & 81.35 \\ YOLOv5s+CLAU & 89.40 & **97.59 (+0.30)** & **85.30 (+5.58)** & **93.84 (+0.70)** \\ LC-YOLO & **92.65 (+2.95)** & **96.87** & **85.78 (+6.06)** & **95.87 (+2.73)** \\ \hline YOLOv5n & 89.13 & 95.80 & 78.07 & 93.85 \\ YOLOv5n+LB & **91.61 (+2.48)** & **97.98 (+2.18)** & **81.27 (+3.20)** & 89.88 \\ YOLOv5n+CLAU & 83.07 & **98.85 (+3.05)** & **88.56 (+10.49)** & 79.21 \\ Tiny LC-YOLO & **91.85 (+2.72)** & **96.72 (+0.92)** & **83.11 (+5.04)** & **95.50 (+1.65)** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Precision and Recall in ablation experiments on the UCAS-AOD dataset. Figure 9: Comparison of confidence with LB and CLAU on YOLOv5s. which can be improved later by optimizing the code. On the other hand, the CLAU module leads to a small increase in learnable parameters, reasoning time consumption, and computation. Nevertheless, its performance results in significantly improvement, as can be seen from Tables 2 and 3. Overall, the LC-YOLO and Tiny LC-YOLO models can enhance network performance while adding minimal parameters, reasoning time, and computation and maintaining a relatively fast detection speed. As shown in the Figure 10, the LB module has a more significant impact on YOLOv5n when detecting smaller samples such as cars. This means that the maximum sharing of prior knowledge in a neural network can to a certain extent make up for the problem of insufficient feature extraction ability in lightweight networks due to the low number of learnable parameters. Compared to the LB model, CLAU improves the performance of YOLOv5s and YOLOv5n more significantly in the detection of small cars. Shallow networks have output characteristics that contain a large amount of detailed information, which can be lost after multiple convolutions and pooling.