# Semi-Supervised Method for Underwater Object Detection Algorithm Based on Improved YOLOv8
## 5 Discussion and Conclusions  
This paper proposes a semi-supervised learning algorithm suitable for the YOLOv8 model, effectively addressing the limitations brought about by the scarcity of labeled data. By incorporating a multi-scale pseudo-label module, the quality of the pseudo-labels generated during the semi-supervised training process is further improved, thereby enhancing the reliability of the semi-supervised training. Additionally, the YOLOv8 algorithm is modified to make it more suitable for underwater object detection tasks. The RFEConv module significantly improves the model's feature extraction ability in underwater images, while the TAE detection head enables the model to effectively detect targets of various scales in underwater environments. Through comparative experiments, the effectiveness of the proposed semi-supervised learning algorithm on YOLOv8 is validated. Ablation experiments were also conducted based on the semi-supervised learning framework. The experimental results show that, on the same dataset, the proposed method achieved an AP of 50.8%, which is an improvement of 11.0% over the fully supervised YOLOv8 algorithm, 12.0% over the fully supervised YOLOv9 algorithm, 14.8% over the fully supervised YOLOv10 algorithm, 11.3% over the fully supervised YOLOv11 algorithm, 9.3% over the semi-supervised Efficient Teacher algorithm, and 3.4% over the semi-supervised Unbiased Teacher algorithm, while requiring only 20% of the computational cost. This demonstrates that the method proposed in this paper is effective for underwater object detection. In summary, the proposed SUD-YOLO method has made significant strides in underwater object detection, showcasing its exceptional performance in addressing the complex factors of underwater environments. By incorporating semi-supervised learning techniques and carefully designed modular optimizations, we have significantly enhanced the model's detection accuracy in data-scarce conditions, providing a more reliable and efficient solution for future underwater object detection tasks. For example, in underwater robotics, the ability to accurately and efficiently detect and classify objects in real time can significantly enhance autonomous navigation and task execution, such as underwater \begin{table}
\begin{tabular}{c c c} \hline \hline
**Thresholds** & \(\mathbf{AP}\) & \(\mathbf{AP_{50}}\) \\ \hline
0.20 & 45.5 & 64.1 \\ \hline
0.30 & 47.1 & 66.3 \\ \hline
0.40 & 49.6 & 70.9 \\ \hline
0.50 & **50.8** & **71.8** \\ \hline
0.60 & 49.8 & 70.7 \\ \hline \hline \end{tabular} The bold font indicates the optimal value in the corresponding metric.  
\end{table} 
Table 5: The effect of different thresholds on MPA. exploration, infrastructure inspection (e.g., pipelines and cables), and marine conservation efforts. This not only reduces the reliance on human divers but also ensures safety and cost-effectiveness in various underwater operations. In particular, in applications such as Autonomous Underwater Vehicles (AUVs) and aquaculture monitoring, this method not only improves detection accuracy across various scales of underwater targets but also substantially reduces the cost of manual labeling in practical applications, thereby facilitating the widespread deployment of underwater intelligent systems. Although our method has demonstrated significant results in various experiments, it still has certain limitations. One notable shortcoming is the lack of pre-training. Pre-training typically leverages large-scale, general-purpose datasets to initialize the model's weights, which can accelerate the training process and enhance the model's generalization ability. Without pre-training, our model relies on training from scratch, which may result in suboptimal performance, particularly when there is a scarcity of data or limited training time. Future work could explore incorporating pretraining strategies into our approach, utilizing expansive, general datasets to initialize the model and then fine-tuning it to improve performance in underwater environments.