## 4 Experiments and Discussions
### Ablation Experiment  
To validate the effectiveness of the various improvements proposed in this study for the EfficientNetV2 backbone, the LPAN module, the CBAM module, the L2 detection head, and the EIOU_loss, comparative ablation experiments incorporating each of the different modules, as well as fusing all of them, were designed in this section using YOLOv5-S as the baseline. As indicated in Table 2, the above experiments used the mAP at two different IOU thresholds, the number of parameters (Params), and the computed floating-point operations per second (FLOPs) as performance evaluation metrics. Analyzing the experimental results, it was found that the conventional CSPDarknet was substituted by EfficientNetV2 as the backbone network. Compared with the original YOLOv5-S model, in the improved network architecture, mAP@.5(%) and mAP@.5:95(%) indexes were increased by 7.5% and 11.3%, respectively, the number of parameters were reduced by 24.4%, and the computing complexity was reduced by 3.09 G, which not only enhanced the target detection precision but also achieved the lightweight of the network. This was due to the strong feature extraction capability of EfficientNetV2, which can mine more deep features to achieve improved precision, and the Fused-MBConv module, which combines multiple residual blocks into a single convolutional layer, which can further reduce the quantities of parameters in the model and the calculation complexity. Further examination of the experimental data indicated that integrating the LPAN module enhanced the model's performance on mAP@.5(%) and mAP@.59(%)by 8.4% and \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**EfficientNetV2** & **LPAN** & **CBAM** & **L2** & **EIOU\_Loss** & **mAP@.5 (\%)** & **mAP@.5.95 (\%)** & **Params (M)** & **FLOPs (G)** \\ \hline \multirow{4}{*}{\(\surd\)} & & & & & 73.4 & 37.9 & 7.26 & 16.92 \\  & & & & & 78.9 (+7.5\%) & (+11.3\%) & 5.49 (\(-\)24.4\%) & **13.83(\(-\)18.3\%)** \\  & \(\surd\) & & & & 79.6 (+8.4\%) & 43.1 & 7.26 & 16.93 (+0.06\%) \\  & & \(\surd\) & & & 80.8 & 44.0 & 6.28 (\(-\)13.5\%) & 14.82 (\(-\)12.4\%) \\  & & & & & (+10\%) & 44.9 & 7.66 (+5.5\%) & 25.41 (+50.2\%) \\  & & & & \(\surd\) & 76.2 (+3.8\%) & 40.7 & 7.26 & 16.92 (\(-\)7.3\%) & (0) & (0) \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & **87.5(+19.2\%)** & **50.2** & **35.2 (\(-\)51.5\%)** & 14.43 (\(-\)14.7\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of experimental results between this method and baseline ablation (data in parentheses are the relative rate of change of each parameter relative to the baseline). 13.7%, respectively. It is worth noting that although the introduction of the LPAN module did not change the parameters of the model, it slightly increased the computational complexity of the model while keeping the number of model parameters unchanged, due to its introduction of hopping connections and feature reuse mechanisms in the network structure. It was a minor expense that was exchanged for a significant improvement in model performance. In the YOLOv5 model, we introduced the CBAM module to process the feature maps to achieve adaptive feature refinement. The CBAM module computes the 1D channel and 2D spatial attention maps sequentially and then multiplies them with the input feature maps, which enhances the depiction of characteristics of obstructed targets on the maps and suppresses the expression of the irrelevant features, thus improving the precision of the model detection. From the results, it can be seen that this module improves the mAP@.5 (%), mAP@.5:95 (%) metrics by 10% and 16%, respectively, which reduces the model's parameter amount by 13.5% and the computational complexity by 2.1 G.  
In order to provide the network with better performance in small target detection tasks, we added the L2 detection head to YOLOv5 for related experiments.