# Semi-Supervised Method for Underwater Object Detection Algorithm Based on Improved YOLOv8
## 4 Results
### Ablation Studies  
This section will progressively validate the effectiveness of the key components of the proposed model, specifically analyzing the impact of the following factors on the performance of the underwater object detection model: (1) the impact of different modules on the model; (2) the impact of different pseudo-label filtering thresholds on the model. Ablation experiments are conducted on the DUO dataset, where 10% of the data are used as labeled data. A total of six network models were validated, and the ablation experiment results are shown in Table 4. The baseline model is a semi-supervised object detection model based on YOLOv8s using the Mean Teacher method. Starting from the baseline model, the RFEConv module was first added, leading to improvements of 1.9% in _AP_, 2.5% in _AP\({}_{50}\)_, and 2.7% in _AP\({}_{5}\)_. Next, the TAE detection head was added, resulting in an increase of 2.7% in _AP_, 3.7% in _AP\({}_{50}\)_, and 3.8% in _AP\({}_{75}\)_. Finally, the multi-scale pseudo-label augmentation module was incorporated, which further improved the performance by 2.2% in _AP_, 2.5% in _AP\({}_{50}\)_, and 1.8% in _AP\({}_{75}\)_.  
Ablation experiments on the 10% labeled DUO dataset. In this ablation study, the baseline model is YOLOv8 equipped with the semi-supervised detection framework proposed in this paper, without any additional modules. The experimental results indicate that, after incorporating the RFEConv module, the model's performance in detecting medium and large objects is significantly improved. This suggests that the RFEConv module enhances the feature extraction capabilities for larger objects, though it may overlook smaller targets. Upon introducing the TAE module, the model demonstrates a substantial improvement in detecting small and medium-sized objects, with a moderate enhancement in the detection of larger objects. This highlights that the TAE module effectively improves the model's ability to detect smaller targets, thereby contributing to an overall increase in detection performance. Lastly, the addition of the MPA module further enhances the model's detection capabilities for large and medium-sized objects, suggesting that MPA facilitates the generation of larger pseudo-labels during the semi-supervised training process. Overall, each module plays a distinct role, and their combined effects have collectively contributed to the superior performance of our method. Table 5 shows the effect of using different thresholds for pseudo-label filtering in the model. In this study, we selected a threshold of 0.5 for pseudo-label filtering. This threshold was chosen based on extensive experimental evaluations, demonstrating that it strikes a reasonable balance between the quality and quantity of pseudo-labels. Specifically, a lower threshold may introduce too many low-quality pseudo-labels, which could negatively impact the training process and degrade model performance. Conversely, a higher threshold, while ensuring better quality pseudo-labels, significantly reduces the amount of training data available, which compromises the effectiveness of semi-supervised learning. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Algorithm** & **Params** & **GFlops** & _AP_ & _AP\({}_{50}\)_ & _AP\({}_{75}\)_ & _AP\({}_{5}\)_ & _AP\({}_{M}\)_ & _AP\({}_{L}\)_ \\ \hline YOLOv8 & 11.1 & 28.7 & 43.4 & 63.1 & 49.7 & 37.3 & 45.7 & 41.9 \\ \hline YOLOv8 + RFE & 11.3 & 29.5 & 45.3 & 65.6 & 52.4 & 33.7 & 46.8 & 44.5 \\ \hline YOLOv8 + RFE + TAE & 9.05 & 34.1 & 48.6 & 69.3 & 56.2 & 38.0 & 51.0 & 46.9 \\ \hline YOLOv8 + RFE + TAE + MPA & 9.05 & 34.1 & 50.8 & 71.8 & 58.0 & 35.5 & 53.1 & 48.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiments on the 10% labeled DUO dataset. Thus, a threshold of 0.5 serves as an effective compromise, ensuring both high-quality pseudo-labels and sufficient training data. Although the 0.5 threshold has yielded promising results in our experiments, we acknowledge that it may not be optimal for all datasets and tasks.