## 4 Experiments and Results
### Standard of Evaluation
#### 4.3.2 Impact of the Preattention Module on SSDD including Offshore and Insbore Scenes  
Using the logic discussed in the earlier sections, this study divided the datasets into two subdatasets, A and B, based on whether or not the target size of the dataset in the experiments is more than 32 \(\times\) 32. The model without the preattention module is known as YOLOv4-MHSA. Experiments were run on both datasets to see if there was a preattention module and how it affected the detection accuracy of Auto-T-YOLO. The detection results of the two accuracy evaluation indicators on YOLOv4, YOLOv4-MHSA, and Auto-T-YOLO on SSDD, including offshore and offshore scenes, are shown in Tables 7-9, respectively. On the SSDD, without the preattention module, indicators such as detection accuracy are lower than in the models with preattention modules. On the SSDD dataset, the accuracy of Auto-T-YOLO is 1.4% greater than that of YOLOv4-MHSA. When compared to YOLOv4, Auto-T-YOLO has a 6.3% higher accuracy. This shows the effectiveness of the preattention module. Moreover, it has been demonstrated that incorporating the biological optic nerve into the working principle of CV image processing is the appropriate research direction. It enables the network mode to replicate the human brain for image processing features, enhancing detection accuracy. Figure 11: Experimental results for models with different target classifications on SSDD. Figure 12: Experimental results for models with different target classifications on offshore scenes. Auto-T-YOLO has the best performance, as shown in Table 8, with 98.33% mAP. When combined with the image characteristics of offshore scenes, the accuracy of Auto-T-YOLO is only 0.23% higher than that of YOLOv4-MHSA. We believe this is because images of offshore scenes have a simple background, making the MHSA mechanism, which focuses more on global information and senses the larger field, unable to extract more valid information. The accuracy of the YOLOv4 model and YOLOv4-MHSA models can be improved by adding the preattention stage. This also indicates that automatically classifying the target size can enhance the accuracy value of target detection. The improvement of the proposed model's accuracy also shows that deep CNN can focus more on effective semantic information for images of small-size targets. The results demonstrate that Auto-T-YOLO is more accurate than the other two models, indicating that the detection method for target size classification is effective. For the inshorne scenes on SSDD, the accuracy and other indicators of Auto-T-YOLO is also higher than those of YOLOv4-MHSA and YOLOv4, as illustrated in Table 8. From our previous analysis of SSDD, it can be seen that there are some complex backgrounds for the inshorne scenes. Accordingly, the YOLOv4-MHSA is 10.63% more accurate than the YOLOv4. After incorporating the MHSA mechanism, more information is retrieved from the features of the ship's target region, compensating for YOLOv4's lack of global information. After including the automatic target size selection mechanism in our model, various network structures are employed for target images of varying sizes. The advantages of the two network structures are pooled, resulting in more accuracy than a single fixed YOLO-MHSA. Moreover, the accuracy of YOLOv4-MHSA is higher than that of YOLOv4, demonstrating the MHSA module's superiority for identifying medium- and large-size objects and further validating it for image capture global features. The accuracy of Auto-T-YOLO is higher than that of YOLOv4-MHSA, demonstrating that, compared to the transformer mechanism, the convolutional network has certain advantages in detecting small-size targets. Using the precedence of global features in visual perception to detect medium- and large-size targets and incorporating the MHSA module into the model to fuse feature information from global to local can considerably increase the accuracy of target identification tasks.