## 4 Experiment Results and Discussions
### Detection Results and Comparison  
In this section, we will present the entire experimental process and the experimental results using several images that enlarge more typical targets, as shown in Figure 8.  
To evaluate the ship detection performance of CRTD, we conducted comparative experiments both horizontally and vertically on a dataset constructed from Gaofen-4 images. We compared the improved RT-DETR algorithm against popular object detectors, including Faster R-CNN, SSD, the YOLO series, and the original RT-DETR, following cloud and haze removal with DehazeFormer-Net. To maintain consistency in model scales, we selected Faster R-CNN with ResNet50, SSD with VGG16, YOLOv5-M, and RT-DETR-R18 as baseline models for our experiments. To rigorously validate the performance of each method, all experiments utilized three-frame stacked data. The training was conducted for 100 epochs, with all other models fine-tuned to their optimal parameters to ensure the best possible results.  
Figure 9 and Table 1 illustrate the precision-recall curves and Average Precision (AP) scores for various methods applied to the Gaofen-4 dataset, with recall thresholds ranging from 0 to 1. Our proposed method demonstrates superior performance compared to other techniques, achieving an overall AP score of 88.9%, which is notably higher than the scores of the other evaluated detectors. The red line in Figure 9 highlights that our method covers a  
larger area under the curve, indicating enhanced detection capabilities. This comprehensive evaluation underscores the effectiveness of our approach in improving detection precision and recall.  
Overall, the network model proposed in this paper demonstrates superior detection performance. We further evaluated the model using 1024 \(\times\) 1024 pixel images. To assess the method's generalization capability, the test images included multiple ship targets and were partially obscured by thin clouds. Figure 10 illustrates the comparative results of various methods. In this figure, green boxes denote ship targets in the original images, red boxes indicate detection results, and blue boxes highlight areas without ship targets. Figure 10 shows that our proposed method performs well even with thin cloud cover. In more complex scenarios with cloud interference and occlusion, SSD has a higher false alarm rate due to insufficient feature extraction, leading to false detections. Compared to YOLONet and Faster R-CNN, these methods are more likely to miss detections under cloud cover, likely due to noise from the clouds. Overall, the proposed network model demonstrates superior detection performance.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **P (\%)** & **R (\%)** & **AP (\%)** \\ \hline Our approach & 94.21 & 83.59 & 88.9 \\ YOLOv5 & 91.67 & 81.26 & 86.46 \\ SSD & 84.98 & 77.76 & 81.33 \\ Faster-RCNN & 85.36 & 78.21 & 81.785 \\ RT DETR & 92.88 & 80.03 & 86.61 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Algorithm performance comparison.  
Figure 8: Visualization of ship detection results: (**a**) Cloud-Connected Target, where the ship is partially obscured by clouds, demonstrating the algorithm’s ability to detect targets in cloud-interference scenarios; (**b**) Densely Distributed Ships in Port, showing detection results in a port with high ship density, highlighting the algorithm’s performance in complex, crowded environments; (**c**) Detection of Ships at Different Scales, showcasing the detection of ships of various sizes, from small vessels to larger ones, evaluating the algorithm’s scalability and accuracy across different target sizes.