# Semi-Supervised Method for Underwater Object Detection Algorithm Based on Improved YOLOv8
## 4 Results
### Experimental Results
This demonstrates that our approach not only retains the advantages in parameter efficiency and computational cost of one-stage algorithms but also significantly enhances detection performance. In comparison to Efficient Teacher, which is designed for one-stage detection tasks, the proposed model achieves improvements of 9.3%, 8.6%, 12.1%, 8.8%, 10.5%, and 7.2% in _AP_, _AP_\({}_{50}\), _AP_\({}_{75}\), _AP_\({}_{S}\), _AP_\({}_{M}\), and _AP_\({}_{L}\), respectively. Efficient Teacher, based on YOLOv5, retains the computational and parameter efficiency of YOLOv5; however, this design inherently limits its overall performance. To validate the generalization ability of the proposed method, we conducted model comparison experiments on the URPC dataset. Similar to the data partitioning in the DUO dataset, we also selected 10% of the labeled data from the URPC dataset for the experiment. The detailed experimental results are shown in Table 3.  
In fully supervised methods, compared to our baseline YOLOv8, our approach outperforms it by 4.9%, 6.4%, 6.7%, 1.9%, 4.3%, and 6.4% in _AP_, _AP_\({}_{50}\), _AP_\({}_{75}\), _AP_\({}_{S}\), _AP_\({}_{M}\), and _AP_\({}_{L}\), respectively. This demonstrates that our method also yields improvements on other underwater datasets. Compared with semi-supervised methods, our approach surpasses \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & **Params** & **GFlops** & _AP_ & _AP_\({}_{50}\) & _AP_\({}_{75}\) & _AP_\({}_{S}\) & _AP_\({}_{M}\) & _AP_\({}_{L}\) \\ \hline \multicolumn{8}{l}{Supervised algorithms} \\ \hline Faster R-CNN & 41.36 & 208 & 22.3 & 52.5 & 13.6 & 9.7 & 20.9 & 24.6 \\ Cascade R-CNN & 69.2 & 236 & 25.3 & 57.3 & 17.0 & 11.4 & 23.3 & 28.1 \\ ATSS & 19.1 & 153 & 22.7 & 52.0 & 14.9 & 10.3 & 21.7 & 24.5 \\ YOLOv5 & **7.06** & **16.3** & 24.8 & 57.0 & 15.9 & 10.5 & 23.5 & 26.6 \\ YOLOv8 & 11.1 & 28.7 & 29.6 & 61.1 & 24.3 & 12.3 & 27.2 & 32.5 \\ YOLOv9 & 9.6 & 38.8 & 31.1 & 62.3 & 26.9 & 13.6 & 28.9 & 34.1 \\ YOLOv10 & 8.0 & 24.5 & 25.6 & 53.2 & 21.4 & 10.7 & 23.4 & 28.6 \\ YOLOv11 & 9.4 & 21.3 & 29.1 & 60.0 & 24.1 & 12.2 & 26.6 & 32.2 \\ \hline \multicolumn{8}{l}{Semi-supervised algorithms} \\ \hline Unbiased Teacher & 41.3 & 173.6 & **36.6** & **73.1** & **31.6** & **18.0** & **34.0** & **40.2** \\ Efficient Teacher & 7.38 & 16.5 & 25.9 & 57.6 & 19.0 & 9.2 & 23.5 & 29.5 \\ Ours & 9.05 & 34.1 & 34.5 & 67.5 & 31.0 & 14.2 & 31.5 & 38.9 \\ \hline \hline \end{tabular} The bold font indicates the optimal value in the corresponding metric, and the blue font indicates the second-best value in the corresponding metric.  
\end{table} 
Table 3: Comparison with other algorithms on the 10% labeled URPC dataset. Efficient Teacher by 8.6%, 9.9%, 12%, 5.0%, 8.0%, and 9.4% in \(AP\), \(AP_{50}\), \(AP_{75}\), \(AP_{S}\), \(AP_{M}\), and \(AP_{L}\), respectively. The results of Efficient Teacher show minimal improvement compared to YOLOv5, indicating poor performance on this dataset. In contrast, the substantial improvements over YOLOv8 demonstrate the effectiveness of our method on this dataset. On this dataset, our method slightly lags behind Unbiased Teacher, but we outperform it in terms of performance. Despite having only 22% of the parameters and 20% of the computational load of Unbiased Teacher, we achieve comparable experimental results, highlighting that our method is a more favorable choice for devices with limited resources, such as underwater drones.