# Light-YOLO: A Study of a Lightweight YOLOv8n-Based Method for Underwater Fishing Net Detection
## 4 Experimental
### Experimental Results  
Based on the above-listed parameters for training, Figure 10 shows how the various loss functions (loss), mean average precision (mAP), and precision and recall metrics change during the training period (epoch) during the training and validation phases. It can be observed from the figure that with the completion of enough training cycles, our model reaches a converged state, which means that the model gradually stabilizes during the learning process and shows good training results.  
Curve of the algorithm reveals an average precision (AP) of 0.857 on specific categories and all categories. This result highlights the high accuracy of the algorithm in target detection. Notably, the algorithm maintains a recall as high as 0.93 even at very low confidence thresholds, demonstrating its ability to identify most instances of positive classes efficiently. In addition, with the F1-confidence curve, the model achieves an F1 score of 0.84 at a confidence threshold of 0.258, a score that balances precision and recall and is particularly suitable for dealing with scenarios with unbalanced data. These combined results demonstrate the excellent performance of Light-YOLO and robustness in target detection tasks.  
To further evaluate the effectiveness of the method proposed in this study in detecting defects in fishing nets, we executed an ablation study on a dataset expanded by a GAN network and compared the results with the YOLOv8n model. To ensure the reliability and transparency of the experimental results, we adopted [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 as the primary metrics for performance evaluation. We utilized Params and GFLOPs as measures of model size and computational speed. See Table 5 below for specific experimental results.  
A comprehensive picture of the model's performance is provided. The precision-recall experimental results clearly demonstrate how each improvement measure gradually enhances the detection performance of the baseline model. First, introducing the CoTAttention mechanism into YOLOv8n, which effectively integrates local and global contextual information, elevated the model's [EMAIL_ADDRESS] metric to 85.9%. After that, the SEAM module is added, which successfully improves the performance to 88.7% without significantly increasing the number of parameters and computation by adopting a twin network structure  
Figure 10: Various losses of the training process and graphs of some metrics.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & \multicolumn{2}{c}{**Module**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**[EMAIL_ADDRESS] (\%)**} & \multirow{2}{*}{**[EMAIL_ADDRESS]-0.95 (\%)**} & \multirow{2}{*}{**GFLOPs**} \\  & & & & & & & & \\ \hline YOLOv8n & & & & 87.6 & 78.1 & 83.7 & 47.6 & 3.0 & 8.2 \\  & \(\surd\) & & & 88.5 & 79.0 & 85.9 & 50.1 & 3.1 & 8.3 \\ Light-YOLO & \(\surd\) & \(\surd\) & & 88.7 & 79.1 & 86.0 & 50.1 & 3.1 & 8.3 \\  & \(\surd\) & \(\surd\) & \(\surd\) & 89.3 & 80.7 & 86.7 & 51.8 & 4.4 & 6.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model ablation experiments.  
with shared weights and PCM modelling at the pixel level. Ultimately, by integrating the lightweight and efficient DA2D module, the completed build of the light YOLO model not only further improved the [EMAIL_ADDRESS] by 0.7% but also achieved a significant reduction in GFLOPs (26.5%), despite a slight increase in the number of parameters. However, this drawback was effectively mitigated due to the substantial decrease in computational load. Ablation study results demonstrated that the Light-YOLO model possesses a lightweight design and powerful modelling capabilities, perfectly aligning with the research objectives for underwater detection. These experiments critically validated the effectiveness of each module, providing important evidence for enhancements in model performance and theoretical advancements.