## 4 Experimental Results and Analysis
### Comparison Experiment
#### 4.3.2 CA Module Fusion Comparison Experiment  
In this paper, we propose three attention mechanism fusion methods. To test the advantages and disadvantages of the three models' detection effects, we train the original YOLOv5 model on the extended dataset using the parameter configurations listed in Table 1. The results of the three fusion experiments of CA and Li are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Epoch** &
\begin{tabular}{c} **Selection of** \\ **Data Sets** \\ \end{tabular} & **Model** & \(\mathbf{Pr}\) & \(\mathbf{Re}\) & \(\[EMAIL_ADDRESS]}\) & _Time (lt)_ \\ \hline \multirow{4}{*}{100} & \multirow{4}{*}{Standard data sets} & Origin & \multirow{2}{*}{0.9178} & \multirow{2}{*}{0.9147} & \multirow{2}{*}{0.9351} & \multirow{2}{*}{4.43} \\  & & YOLOv-5s & & & & \\  & & Proposed & & & & \\  & & Origin & & & & \\  & Extended dataset & YOLOv-5s & & & & \\  & & Proposed & & & & \\ \hline \multirow{4}{*}{300} & \multirow{4}{*}{Standard data sets} & Origin & \multirow{2}{*}{0.9286} & \multirow{2}{*}{0.9206} & \multirow{2}{*}{0.9434} & \multirow{2}{*}{13.28} \\  & & YOLOv-5s & & & & \\  & & Proposed & & & & \\  & & Origin & & & & \\  & Extended dataset & YOLOv-5s & & & & \\  & & Proposed & & & & \\ \hline \multirow{4}{*}{500} & \multirow{4}{*}{Standard data sets} & Origin & \multirow{2}{*}{0.9354} & \multirow{2}{*}{0.923} & \multirow{2}{*}{0.9485} & \multirow{2}{*}{22.16} \\  & & YOLOv-5s & & & & \\ \cline{1-1}  & & Proposed & & & & \\ \cline{1-1}  & & Origin & & & & \\ \cline{1-1}  & Extended dataset & YOLOv-5s & & & & \\ \cline{1-1}  & & Proposed & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of metrics before and after expanding the dataset.  
The proposed CA module can improve the network's feature extraction capability by maintaining feature location information and suppressing irrelevant features. However, its effectiveness varies depending on the location. Compared to the original YOLOv5 model, YOLOv5-B improves precision by 0.0062 and mean average precision by 0.0093 but recall decreases by 0.0097. YOLOv5-N achieves the highest average precision among the four models, with precision, recall, and average precision improved by 0.0326, 0.0235, and 0.0202, respectively, compared to the original YOLOv5. In contrast, YOLOv5-H's precision, recall, and average precision are slightly lower than the original YOLOv5, and it has increased computation cost.  
The experimental results indicate that the precision, recall, and average precision are not effectively improved by adding the attention mechanism to any position in the network. In the backbone, a large number of image features are extracted with slight redundancy, and the attention mechanism can help the network perform better feature analysis. However, the feature maps of different scales are not integrated, resulting in insufficient feature extraction and a failure to improve the recall rate. In the neck network, features are integrated from the bottom to the deep layer and from the deep to the shallow layer, allowing for better feature extraction and reconstruction through the attention mechanism for channel attention. This improves the network's precision rate, recall rate, and mean average precision. In the prediction stage, a large portion of the features are lost, making it difficult for the attention mechanism to distinguish important information from the highly fused feature map, resulting in reduced performance in all three indicators. Based on the experimental results, YOLOv5-N is the optimal choice.