## 4 Results and Discussion  
We compared the different roles of the various improved modules through comparative experiments. In the training, due to improvements in the backbone part, all of the training processes didn't used the official pre-training model. The size of the input images is 640 \(\times\) 640. A total of 300 rounds of training were used. The training would be stopped if the continuous 50 rounds were verified without any improvement. On the basis of this experiment, the optimal model for comparison was selected. According to the experiment, the optimal model comparison is shown in Table 3.  
From the table, it can be seen that when the edge loss function is changed from CIoU to SIoU, the recall and mAP50 are improved, which is due to the fact that compared to the previous design, the angle between the two frames is taken into account more in addition to the overlapping region, distance, and length and width. With almost no change in the number of parameters (Params), SIoU obtains an increase in precision and recall, which meets the demand of high precision and high recall for surface vessel detection. Meanwhile, the introduction of angle accelerates the convergence of the concept of distance, thus making the convergence faster and preventing the network from hovering around the optimal point, so all subsequent experiments used SIoU as the loss function. Compared with only using SIoU after adding the RFE module, the mAP50 is further improved to 0.972, mAP50:95 to 0.681, and the recall to 0.943. The main reason is that the repeated use of pooling layer in the SPPF module loses detailed information, and it is easy to misidentify the target as background. After the introduction of the RFE module, more details can be retained while reinforcing the semantic information, which improves the performance of the model, resulting in an increase in both precision and recall. After adding the lightweight convolutional GSConv, a more significant reduction in the number of parameters can be seen, while the accuracy, recall, and mAP50:95 continue to improve. After GSConv is combined with VOV-GSCSP to form the slim-neck module, the number of parameters is also comparable to that before it was added, while the accuracy, recall, and mAP50:95 still improve. The reason is that the lightweight convolutional GSConv can better balance the accuracy and speed of the model, and after forming the slim-neck module, this makes it possible to achieve higher computational cost benefits without a significant increase in the number of parameters. After the final addition of coordinate attention mechanism, all of the indexes were greatly improved. The introduction of coordinate attention mechanism further enhanced the important features in the network, and comprehensively improved the model detection ability of the network, which helps to detect more targets and localize them more accurately. The improved YOLOv5s has faster convergence speeds and better accuracy. A comparison of the training process between YOLOv5 and the improved YOLOv5 algorithm is shown in Figure 7.  
In order to more intuitively reflect the difference in the performance of the above detection algorithms, a portion of the detected images are selected for demonstration, as shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **mAP50** & **mAP50:95** & **Params** & **FPS** \\ \hline YOLOv5 & 0.943 & 0.929 & 0.966 & 0.679 & 7M & 52.08 \\ YOLOv5 + SIoU & 0.941 & 0.943 & 0.968 & 0.671 & 7M & 55.25 \\ YOLOv5 + SIoU+RFE & 0.942 & 0.943 & 0.972 & 0.681 & 7.8M & 50.51 \\ YOLOv5 + SIoU + RFE + GSConv & 0.944 & 0.945 & 0.972 & 0.689 & 7.4M & 52.63 \\ YOLOv5 + SIoU + RFE + GSConv + VOV-GSCSP & 0.948 & 0.949 & 0.971 & 0.7 & 7.9M & 50.00 \\ YOLOv5 + SIoU + RFE + GSConv + VOV-GSCSP + CA & **0.957** & **0.955** & **0.978** & **0.71** & 7.9M & 49.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of evaluation indexes of different training models.