# Semi-Supervised Method for Underwater Object Detection Algorithm Based on Improved YOLOv8
## 4 Results
### Experimental Results  
To verify the effectiveness of the proposed method for underwater object detection tasks, we utilized the DUO dataset, with 10% designated as the labeled dataset and the remaining 90% as the unlabeled dataset. The results are shown in Table 2. The proposed method was compared against state-of-the-art general object detection algorithms and two leading semi-supervised object detection methods. These methods include Faster R-CNN [2], ATSS [40], YOLOv7 [41], YOLOv8, YOLOv9 [42], YOLOv10 [43], YOLOv11 [44], Unbiased Teacher [18], and Efficient Teacher [22]. On the 10% labeled DUO dataset, our method outperforms the current state-of-the-art one-stage object detection algorithms. Compared to YOLOv8, our method exceeds by 11.0%, 12.5%, 13.5%, 9.7%, 11.9%, and 9.6% in \(AP\), \(AP_{50}\), \(AP_{75}\), \(AP_{S}\), \(AP_{M}\), and \(AP_{L}\), respectively. When compared to YOLOv11, our approach achieves improvements of 14.8%, 18.0%, 18.5%, 4.7%, 15.4%, and 14.3% in the same metrics. Additionally, in comparison to YOLOv11, our model surpasses by 11.3%, 12.1%, 14.7%, 8.0%, 12.2%, and 10.4%. From the comparison with YOLOv8, it is evident that our method shows more significant improvements in detecting medium and small objects, indicating that our model is better suited to \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Algorithm** & **Params** & **GFlops** & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AP_{S}\) & \(AP_{M}\) & \(AP_{L}\) \\ \hline Supervised algorithms & & & & & & & & \\ \hline Faster R-CNN & 41.36 & 208 & 29.3 & 49.5 & 30.6 & 20.8 & 29.6 & 29.6 \\ Cascade R-CNN & 69.2 & 236 & 30.7 & 51.2 & 32.0 & 27.1 & 31.0 & 30.7 \\ ATSS & 19.1 & 153 & 37.7 & 61.1 & 40.1 & 35.0 & 38.5 & 37.8 \\ YOLOv5 & 7.06 & 16.3 & 35.9 & 58.4 & 38.8 & 23.9 & 37.1 & 35.1 \\ YOLOv7 & **6.02** & **13.0** & 33.4 & 55.8 & 25.4 & 22.6 & 35.5 & 31.6 \\ YOLOv8 & 11.1 & 28.7 & 39.8 & 59.3 & 44.5 & 25.8 & 41.2 & 39.3 \\ YOLOv9 & 9.6 & 38.8 & 38.8 & 58.8 & 43.5 & 26.3 & 41.8 & 36.1 \\ YOLOv10 & 8.0 & 24.5 & 36.0 & 53.8 & 39.5 & 30.8 & 37.7 & 34.6 \\ YOLOv11 & 9.4 & 21.3 & 39.5 & 59.7 & 43.3 & 27.5 & 40.9 & 38.5 \\ \hline \hline \multicolumn{10}{l}{Semi-supervised algorithms} \\ \hline Unbiased Teacher & 41.3 & 173.6 & 47.4 & 71.0 & 52.6 & 34.6 & 48.2 & 47.7 \\ Efficient Teacher & 7.38 & 16.5 & 41.5 & 63.2 & 45.9 & 26.7 & 42.6 & 41.1 \\ Ours & 9.05 & 34.1 & **50.8** & **71.8** & **58.0** & **35.5** & **53.1** & **48.9** \\ \hline \hline \end{tabular} The bold font indicates the optimal value in the corresponding metric.  
\end{table} 
Table 2: Comparison with other algorithms on the 10% labeled DUO dataset. underwater environments, which are typically filled with a large number of medium and small targets. Among the three two-stage object detection algorithms, ATSS performs the best. However, our model still outperforms ATSS by 13.1%, 10.7%, 17.9%, 0.5%, 14.6%, and 11.1% in _AP_, _AP_\({}_{50}\), _AP_\({}_{75}\), _AP_\({}_{S}\), _AP_\({}_{M}\), and _AP_\({}_{L}\), respectively. The difference in the _AP_\({}_{S}\) metric is minimal, suggesting that there is still room for improvement in small object detection performance. For other metrics, our model maintains a clear advantage, and it also has significantly fewer parameters and lower computational cost. Specifically, the computation cost of our model is only 22.2% of that of ATSS. We conducted a comparison with two general semi-supervised object detection algorithms on the same dataset. One is the Unbiased Teacher, designed for two-stage object detection, and the other is Efficient Teacher, tailored for one-stage detection. Compared to the Unbiased Teacher model, the proposed method achieves improvements of 3.4%, 0.8%, 5.4%, 0.9%, 4.9%, and 1.2% in _AP_, _AP_\({}_{50}\), _AP_\({}_{75}\), _AP_\({}_{S}\), _AP_\({}_{M}\), and _AP_\({}_{L}\), respectively. Furthermore, the number of parameters in the proposed model is only 23% that of the Unbiased Teacher, and its computational complexity (FLOPs) is reduced to just 19.6%.