## 4 Results
### Ship Target Detection Experiment  
To evaluate the performance of the Bi-YOLO algorithm in complex scenarios, we conducted comparative experiments on the FLShip dataset using various benchmark algorithms. These included Deformable-DETR [50], RTMdet [51], YOLOv7 [52], YOLOv8s, and the original YOLO1s. Evaluation metrics included [EMAIL_ADDRESS], [EMAIL_ADDRESS], GFLOPs, Params, and model size. The mAP is a comprehensive evaluation metric for object detection system performance, allowing for reliable comparison between different algorithms or models across various datasets. [EMAIL_ADDRESS] operates by calculating the average precision (AP) for all images in the first class when the IoU threshold is set to 0.5, and then repeating the process for the remaining classes. The average of these AP values gives the mAP score. [EMAIL_ADDRESS]:0.95 is an extension of mAP, which calculates multiple AP values within the IoU range from 0.5 to 0.95 (with a step size of 0.05) and averages them. Specifically, [EMAIL_ADDRESS]:0.95 provides a more comprehensive assessment of model performance across varying strictness conditions by considering multiple IoU thresholds. Compared to the single [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95 demands that the model maintain high detection accuracy at higher IoU thresholds, thus better reflecting the model's robustness and accuracy in complex scenarios. In addition to precision metrics, the computational efficiency of the model is an important factor in assessing its practical value. GFLOPs reflect the computational complexity of the model, while Params directly determine the model's storage and memory usage. In practical applications, especially on resource-constrained edge computing devices, achieving lower GFLOPs and Params is crucial for improving deployment efficiency. This study conducted object detection model training based on the FLShip dataset. The dataset comprises 6286 images of MASSs captured in both inland and maritime environments. To enhance the model's generalization capability and reduce the risk of overfitting, data augmentation techniques, including random brightness adjustment (\(\pm\)30%) and Gaussian noise injection (\(\sigma\) = 0.05), were applied to improve the model's adaptability in complex environments. The dataset was split into training, validation, and test sets at a ratio of 76:16:8 to ensure sufficient training data. This experiment did not use external datasets for validation, focusing instead on the unique characteristics of the FLShip dataset. This dataset is highly  
Figure 12: The data samples from the FLShip dataset. representative of real-world application scenarios, containing a rich variety of MASS target information along with complementary E-AIS data, making the experimental results more practically valuable. As shown in Figure 13, we analyzed the performance of Bi-YOLO and mainstream detection models during training by examining the [EMAIL_ADDRESS] metric change curve. The experimental results demonstrate that, in the early stages of training, Bi-YOLO exhibited a performance improvement trend similar to YOLO11s and YOLOv8s, significantly outperforming YOLOv7, Deformable-DETR, and RTMdet-l. As training progressed, Bi-YOLO's detection performance gradually surpassed that of other algorithms, with a 0.4% mAP advantage over YOLO11s and a gap of more than 3.4% compared to traditional architectures like Deformable-DETR. Overall, Bi-YOLO not only showed a faster convergence speed during training but also demonstrated stronger competitiveness in final detection performance, confirming its advantages in object detection tasks. To further demonstrate the effectiveness of the proposed method, we compared the Bi-YOLO model with YOLO11s, YOLOv8s, YOLOv7, Deformable-DETR, and RTMdet on the FLShip validation set.