## 4 Experiments and Results
### Standard of Evaluation
#### 4.3.1 Target Size Threshold Setting  
The study trains Auto-T-YOLO on SSDD (including offshore and inshorse scenes). In the preattention module, Auto-T-YOLO divides the entire dataset into two subdatasets, A and B, based on the target size with an explanation of the target size in the COCO dataset. Due to the difference between the object characteristics in remote sensing and the COCO dataset, the classification process cannot fully operate depending on the definition of the target size for the COCO dataset. Therefore, to identify the appropriate target size for the actual case, the experiments were based on the large-size target (64 \(\times\) 64) and medium-size target (32 \(\times\) 32) to train the dataset. The training results illustrated in Table 4 indicate that splitting the dataset based on the target size (32 \(\times\) 32) is more appropriate for remote sensing images and more efficient in detection.  
As shown in Table 2, there are a few large-size targets defined by the COCO dataset in the remote sensing images. As shown in Tables 4-6, the scores for \(Auto-T-YOLO_{32\times 32}\) are all higher than those of \(Auto-T-YOLO_{64\times 64}\). In the inshorse scenes, our model has a much higher accuracy than the YOLOv4 model because the ship target size in the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & \(AP\) & _Recall_ & _Precision_ & \(F1\) & \(Pd\) & \(Pm\) & _Pf_ \\ \hline YOLOv4 & 90.15\% & 0.86 & 0.94 & 0.90 & 0.86 & 0.14 & 0.15 \\ \hline
92.48\% & 0.92 & 0.89 & 0.90 & 0.92 & 0.08 & 0.07 \\ \hline
96.3\% & 0.96 & 0.86 & 0.91 & 0.96 & 0.04 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results for models with different target classifications on SSDD.  
inshore scenes is slightly larger. We can also incorporate a transformer mechanism to make it concentrate more on the useful information of the object and extract the essential semantic characteristic information from the overall information because the complicated background has an impact. For offshore scenes, due to the simple background, the characteristics of the area near the ship targets do not contribute much to the target itself. The original YOLOv4 model already has an ideal detection accuracy. The number of images with a target-size area >32 \(\times\) 32 is three times greater than the number of images with a target-size area > 64 \(\times\) 64 in Table 2, indicating that the performance of \(Auto-T-YOLO_{32\times 32}\) is superior to those of \(Auto-T-YOLO_{64\times 64}\) after adding the MHSA module, and the detection accuracy of medium-size targets can be increased once the MHSA module is added. It can be seen that the associated modeling of global semantic information can also supplement the feature information of targets that was lost in the deep CNN. Therefore, Tables 4-6 exhibit a combination of experimental findings and target size features from the remote sensing dataset. As shown in Figures 10-12, the histograms show the effect of different target size classifications on the experimental results. To employ the SPP-MHSA model for feature fusion extraction, we recommend setting the target area to 32 \(\times\) 32 in the preattention module stage. If all target sizes in the images are less than 32 \(\times\) 32, the image features should be processed using the YOLOv4 module.