## 4 Experiments and Results
### Comparison with SOTA Models  
To validate this study's findings, Auto-T-YOLO was compared to current SOTA models. The field of target detection was introduced to remote sensing imaging. In addition, two accuracy assessment measures were used to assess the effectiveness of the detecting model. Figure 14 depicts the visualization results of different detection models on inshore and offshore scenes from the SSDD dataset. The following discussions and conclusions are based on Tables 10-12. In Table 10, the Auto-T-YOLO model has the highest accuracy compared to the other SOTA models. In terms of accuracy, Auto-T-YOLO is 2.1% is better than the Retinanet model, which is the second best. Model detection is also faster than Retinanet. However, the added characteristics and computational complexity of the model cause a drop in speed compared to one-stage detectors (YOLOv2 and YOLOv3). However, the proposed model is more accurate. Compared to two-stage detection systems, Auto-T-YOLO has fewer parameters, reduced computing complexity, and higher accuracy. Although the FPS of our proposed model is not the highest, as shown in Table 10, yet, when compared to the highest FPS of YOLOv2, the model's detection accuracy improves by 6% without significantly increasing detection time. Based on the above discussions, it can be concluded that improving Auto-T-YOLO results in a notable improvement in accuracy. As shown in Table 11, while the accuracy of Auto-T-YOLO is 0.07% lower than that of the ARPN model, it is 1.6 times faster in terms of operating speed. In addition to the ARPN model, Auto-T-YOLO outperforms Retinanet by 0.32% and is 1.7 times  
Figure 14: Visual detection results of ships for different models in the SSDD dataset. faster. Speed and accuracy are important indicators for assessing the model's performance in terms of real-time target detection. There is sufficient evidence that suggests that Auto-T-YOLO optimally balances the relationship between accuracy and speed in real-world application scenarios. The results in Table 12 show that Auto-T-YOLO has the highest accuracy in inshorc scenarios. The AP of 91.87% achieves gains of 11.78%, 10.64%, 9.68%, and 7.68% using HR-SDNet, Fater RCNN, RetinaNet, and ARPN, respectively, with an accuracy rate of more than 80%. On inshorc scenarios, our model outperforms the ARPN model. This demonstrates that for complicated backgrounds and large-size targets, the approaches of automatically selecting the suitable structure based on image size and the MHSA mechanism for feature extraction from global to local provide a more successful approach. Therefore, combining the transformer mechanism and the deep CNN is feasible to perform a multiscale fusion of features from the global to the local. Moreover, a model that simulates human visual selective attention mechanisms can be used to improve object detection accuracy. Comparison with detection results of different SOTA algorithms in the SSDD (containing inshorc and offshore scenarios) reveals that Auto-T-YOLO has strong generalization capability. Compared to SOTA models, Auto-T-YOLO achieves the appropriate balance of accuracy and speed and the optimal speed and accuracy. In Figure 14, the first three rows are inshorc scenes. Others are offshore scenes. The red detection box represents the ship target not detected by the detection model. These visualization results also corroborate what is shown in the tables above. Compared with the SOTA method, Auto-T-YOLO has the highest accuracy. In inshorc scenes, Auto-T-YOLO showed a significantly reduced missed detection rate (1-recall) compared with the values of YOLOV2, YOLOV3, and SSD. The accuracy rate for the offshore scenes was also significantly improved. These visualization results also represent the superiority of the model.