# Fish Detection in Fishways for Hydropower Stations Using Bidirectional Cross-Scale Feature Fusion
## 3 Results and Analysis
### Ablation Experiment  
To verify the effectiveness of the enhanced modules in the proposed method, ablation experiments were conducted on the Fish26 dataset. Using the YOLOv8n network as the baseline, each improvement module was sequentially added to the network. The impact of different modules on the final object detection performance was assessed through ablation experiments. The results of these experiments are presented in Table 3. Group 1 represents the model YOLOv8n, while Groups 2 to 4 incorporate individual improvements to the baseline model. The results indicate that replacing the C2f module in the backbone network with the C2f-Faster-EMA module increases [EMAIL_ADDRESS] by 1.5%, reduces model parameters by 8.4%, and decreases computational cost. The modifications make the model more lightweight and improve feature extraction efficiency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **P** & **R** & **[EMAIL_ADDRESS]** & **Parameters/M** & **GFLOPs** \\ \hline \multirow{4}{*}{YOLOv8 n +} & CIOU & 0.88 & 0.894 & 0.901 \\  & SIOU & 0.863 & 0.87 & 0.872 \\  & EIOU & 0.876 & 0.887 & 0.893 \\  & GIOU & 0.886 & 0.894 & 0.905 \\  & WIOU & 0.892 & 0.916 & 0.918 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detection results of the proposed method with different bounding box loss functions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P** & **R** & **[EMAIL_ADDRESS]** & **Parameters/M** & **GFLOPs** \\ \hline Faster R-CNN & 0.764 & 0.828 & 0.821 & 72.1 & 682 \\ SSD & 0.796 & 0.832 & 0.852 & 25.02 & 263.7 \\ YOLOv5n & 0.886 & 0.889 & 0.892 & 2.32 & 5.1 \\ YOLOv6n & 0.872 & 0.863 & 0.872 & 3.21 & 8.6 \\ YOLOv7-Tiny & 0.862 & 0.892 & 0.883 & 5.42 & 13 \\ YOLOv8n & 0.882 & 0.877 & 0.901 & 3.08 & 8.1 \\ proposed & 0.892 & 0.916 & 0.918 & 2.34 & 6.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with the state-of-the-art methods on the dataset.  
After using RepBiPAN to improve the neck component, although the [EMAIL_ADDRESS] showed only a slight increase, model parameters decreased by 15.9%, and the computation speed improved, demonstrating the effectiveness of the RepBiPAN-enhanced Neck. The WIoU loss function further boosts detection accuracy by intelligently adjusting the IoU threshold, effectively reducing the negative impact of low-quality samples on model performance.  
Groups 5 to 7, which incorporate two different modules each, provide further evidence of the effectiveness of each module. Group 8 represents the model with all modules applied. In this configuration, the C2f module in the backbone network is replaced by the C2f-Faster-EMA module, the Neck is improved with RepBiPAN, and CIoU is replaced by Wise-IoU as the bounding box regression loss function. This combination not only maintains the model's lightweight nature but also enhances detection accuracy and recall.  
Based on the validation of the effectiveness and advancement of the proposed method improvement strategy, the model was trained on a prepared dataset, with the results shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Number** & **C2f-Faster-EMA** & **RepBiPAN** & **Wise-IoU** & **P** & **R** & **[EMAIL_ADDRESS]** & **Parameters/M** & **GFLOPs** \\ \hline
1 & - & - & - & 0.882 & 0.877 & 0.901 & 3.08 & 8.1 \\
2 & \(\surd\) & - & - & 0.919 & 0.894 & 0.916 & 2.82 & 7.3 \\
3 & - & \(\surd\) & - & 0.912 & 0.904 & 0.909 & 2.59 & 7.2 \\
4 & - & - & \(\surd\) & 0.898 & 0.895 & 0.913 & 3.12 & 8.5 \\
5 & \(\surd\) & \(\surd\) & - & 0.912 & 0.909 & 0.92 & 2.68 & 7.5 \\
6 & \(\surd\) & - & \(\surd\) & 0.92 & 0.912 & 0.919 & 2.82 & 7.4 \\
7 & - & \(\surd\) & \(\surd\) & 0.913 & 0.908 & 0.907 & 2.61 & 6.9 \\
8 & \(\surd\) & \(\surd\) & \(\surd\) & 0.892 & 0.916 & 0.918 & 2.34 & 6.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation indicators for the proposed method ablation study.  
Figure 6: The [EMAIL_ADDRESS] value curve of different detection algorithms after training and testing on the Fish26 dataset.