## 5 Discussion and Conclusions  
The current study addresses the issue of edge features for ship objects with large sizes or irregular aspect ratios, which result in poor detection accuracy. Moreover, deep CNN only mechanically learns image features and cannot automatically update the structure with the image features during computation. To solve these issues, the present study introduced an enhanced object detection model that combines the features of the transformer mechanism and CNN. To overcome the problem of the target's edge features, which are easily ignored, feature fusion is accomplished from global to local for large-size images and medium-size targets. The proposed method, which simulates the human visual attention pattern, chooses distinct structures based on image attributes for target detection. Moreover, the adopted Auto-T-YOLO model autonomously selects different structures to process different image information to improve the model's detection level. The results showed that once the network structure is incorporated into the preattention and attention stages, the detection accuracy improves, providing some interpretability for the deep CNN's black-box operation.  
Inshore and offshore images for remote sensing have distinct characteristics. Offshore images feature small ship targets against plain backgrounds, and inshorc images feature larger ship targets against complex backgrounds. As a result, individually detecting various types of images can show the model's adaptability and usefulness. In addition, the study used two distinct assessment indexes to validate the detection performance, both of which improved the detection accuracy of the YOLOv4 model. Compared to the SOTA technique, the experimental findings proved the efficacy and superiority of Auto-T-YOLO over existing SOTA ship detectors in terms of target detection.  
Although this study's findings indicated that the proposed model achieves high detection accuracy, there is still potential for improvement. Due to model limitations, it is impossible to change the position of the inspection frame to the ship's bow clew angle for more accurate detection. Researchers should focus on better integrating the transformer mechanism with the CNN in the future to improve decontamination accuracy and make the network structure consciously detect and process the ship based on its bow clew angle. There is also potential to improve the model's detection accuracy on inshorc images. Meanwhile, the model's framework will be modified to lower the algorithm's complexity in the subsequent study.  
Conceptualization, B.S. and X.W.; methodology, B.S. and X.W.; formal analysis, B.S. and F.D.; investigation, B.S., X.W. and A.O.; resources, X.W. and F.D.; data curation, B.S. and A.O.; writing--original draft preparation, B.S. and X.W.; writing--review and editing, B.S., X.W. and A.P.; visualization, B.S. and X.W.; supervision, X.W.; project administration, B.S., X.W. and A.P.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant 61872231 and Grant 61701297.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & _AP_ & _Recall_ & _Precision_ & _F1_ & _Pd_ & _Pm_ & _Pf_ & _FPS_ & **Param(M)** \\ \hline YOLOv3 & 78.50\% & 0.71 & 0.73 & 0.72 & 0.71 & 0.29 & 0.30 & 20.1 & 243.5 \\ \hline Fater R-cnn & 81.14\% & 0.82 & 0.63 & 0.71 & 0.82 & 0.18 & 0.14 & 13.3 & 482.4 \\ \hline RetinaNet & 82.1\% & 0.83 & 0.86 & 0.84 & 0.83 & 0.27 & 0.18 & 12.8 & 442.3 \\ \hline DAPN & 77.20\% & 0.72 & 0.69 & 0.70 & 0.72 & 0.28 & 0.27 & 24 & 244.2 \\ \hline HR-SDNet & 80.0\% & 0.84 & 0.66 & 0.73 & 0.84 & 0.26 & 0.16 & 11 & 598.1 \\ \hline ARPN & 84.1 \% & 0.85 & 0.73 & 0.79 & 0.85 & 0.15 & 0.13 & 13 & 490.7 \\ \hline Auto-T-YOLO & 91.78\% & 0.89 & 0.93 & 0.91 & 0.89 & 0.11 & 0.13 & 20.9 & 247.8 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Experimental results of SOTA models on inshorc scenes.