## 4 Results
### Ship Target Detection Experiment
The experimental results are shown in Table 4. As observed, Bi-YOLO outperforms the other models in both [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 metrics, achieving 98.7% in [EMAIL_ADDRESS] and 79.6% in [EMAIL_ADDRESS]:0.95, with improvements of 0.4 and 0.5 percentage points over YOLO11s, respectively, demonstrating the model's comprehensive capability in target detection under various conditions. As shown in Figure 14, we present a comparison of the detection performance between YOLO11s and Bi-YOLO on the same set of images. In Figure 14c, YOLO11s missed detection because the target ship was far away and the light was dim, which caused the ship to be confused with the background; in Figure 14b,d, YOLO11s also failed to detect the target due to fog interference and the ship being far away. Bi-YOLO showed excellent robustness and adaptability when dealing with complex environments, especially in the detection of small targets at long distances in inland waters and in the sea environment due to interference caused by fog and waves. Bi-YOLO can still accurately identify all targets in these complex environments. This advantage is mainly attributed to the introduced C2f_BiFormer module, which enhances feature representation and fine-grained target feature extraction, enabling the model to perform more stably in detecting small, long-range targets, ensuring precise target capture even in complex, low-visibility scenarios. Figure 13: Training [EMAIL_ADDRESS] curves for Bi-YOLO and various object detection algorithms. Figure 14: (a–f) respectively show the comparison of detection effects between YOLO11s and Bi-YOLO. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{5}{c}{**Evaluation Metrics**} \\ \cline{2-5}  & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]/\%** & **Model** & \multirow{2}{*}{**Params/106 (\(\downarrow\))**} & \multirow{2}{*}{**GFLOPs (\(\downarrow\))**} \\  & **(\(\uparrow\))** & **(\(\uparrow\))** & **Size/MB (\(\downarrow\))** & & \\ \hline Bi-YOLO & \multirow{2}{*}{95} & \multirow{2}{*}{79.6} & \multirow{2}{*}{17.4} & \multirow{2}{*}{8.9} & \multirow{2}{*}{19.4} \\ (ours) & & & & & \\ YOLO11s & 98.3 & & & & \\ YOLO08s & 97.8 & & & & \\ YOLO07 & 97.7 & & & & \\ Deformable- & \multirow{2}{*}{95} & \multirow{2}{*}{74.3} & \multirow{2}{*}{82.5} & \multirow{2}{*}{79.951} \\ DETR & & & & & \\ RTMdet-l & 95.3 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation results of ship detection algorithms on the FLShip validation set. Compared to the classic Deformable-DETR and RTMdet-l, Bi-YOLO has demonstrated significant improvements in detection capabilities in complex scenarios. For instance, the [EMAIL_ADDRESS]:0.95 metric improved by 18.3 percentage points over Deformable-DETR. This improvement is particularly important because [EMAIL_ADDRESS]:0.95 better reflects the model's precision in locating small targets under high IoU conditions. Despite Bi-YOLO's excellent performance across multiple accuracy metrics, model complexity and real-time performance remain key considerations in practical applications, especially in embedded hardware environments. In the experiments, Bi-YOLO achieved 19.4 GFLOPs, with 8.9 M parameters and a model size of 17.4 MB, reaching an FPS of 120. Compared to the latest YOLO11s, Bi-YOLO achieves significant optimizations in model size, number of parameters, and computational complexity. Specifically, the model size of Bi-YOLO is reduced by 9.3%, the number of parameters is decreased by 5.4%, and the computational complexity is lowered by 8.9%. These optimizations enhance Bi-YOLO's application potential, making it particularly suitable for environments with limited computing resources.