## 4 Experiment Results and Discussions
### Experimental Analysis  
In evaluating the performance of object detection methods, we employ multiple metrics to ensure a comprehensive assessment. Key metrics include Average Precision (AP), precision, and recall. Precision (P) measures the proportion of correct samples among all predicted results. A precision of 100% indicates that all predictions are accurate, with no false positives, reflecting a high reliability in the model's predictions. Recall (R), on the other hand, focuses on the proportion of correctly identified positive samples among all actual positive instances. A recall of 100% signifies that no targets have been missed, meaning all detectable objects have been successfully identified. This metric is crucial for assessing the model's effectiveness in recognizing all targets in complex environments.Additionally, we incorporate Intersection over Union (IoU) to evaluate ship detection performance. IoU quantifies the accuracy of predictions by calculating the ratio of the overlapping area  
Figure 7: Schematic diagram of training samples for ship detection. The dataset includes a wide range of ship targets, varying in type (cargo ships, warships, and passenger ships) and size (from small vessels occupying 2 to 6 pixels to larger ones). It covers diverse scenarios, such as busy ports with complex backgrounds and open seas with varied lighting and sea conditions. The dataset features both static ships (anchored or docked) and dynamic ships in motion, providing a comprehensive resource for training robust ship detection models.  
between detected objects and ground truth objects. A higher IoU value indicates a greater overlap between the model's predictions and the actual targets, providing a more accurate measure of detection capability. The formula for calculating IoU is as follows:  
\[\text{IoU}=\frac{\text{Area of Overlap}}{\text{Area of Union}}\]  
\[\text{IoU}=\frac{\left|\text{A}_{\text{P}}\cap\text{A}_{\text{GT}}\right|}{ \left|\text{A}_{\text{P}}\cup\text{A}_{\text{GT}}\right|} \tag{8}\]  
A\({}_{\text{P}}\) and A\({}_{\text{GT}}\) denote the areas of the predicted and ground truth bounding boxes, respectively. A higher IoU value signifies a greater degree of overlap between these boxes, leading to improved detection accuracy. An IoU threshold, commonly set at 0.5, is used; if the IoU value exceeds this threshold, the detection is deemed correct.  
Moreover, relying solely on precision and recall for model evaluation is inadequate. For example, while model A may demonstrate higher precision than model B, it could have lower recall. To tackle this, precision and recall are combined to derive another important metric: Average Precision (AP) score, which quantitatively assesses detection accuracy. AP represents the area under the precision-recall curve, calculated by adjusting the thresholds to obtain varying precision and recall values. It is generally expressed by the following formula:  
\[\text{Precision}=\frac{TP}{TP+FP}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{9}\]  
\[AP=\int_{0}^{1}P(R)dR\]  
In this context, the metrics \(TP\), \(FP\), and \(FN\) indicate the quantities of true positives, false positives, and false negatives, respectively. Specifically, \(TP\) refers to the number of correctly detected objects; \(FP\) denotes the number of non-targets incorrectly identified as targets; and \(FN\) represents the number of targets that were not detected. Average Precision (AP) provides an accurate and intuitive measure of the detection model's performance. For multi-class detection tasks, we use mean Average Precision (mAP), which is the average of the AP values across all classes. This approach aims to comprehensively evaluate the model's detection capabilities for each category, thus offering a holistic view of its overall performance. It is important to note that in the context of this paper, which focuses on ship target detection, the computed mAP is equivalent to AP.