# An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images
## 4 Experiments
### Ablation Study
We further conduct an experiment to validate how the number of multi-scale feature extraction branches affects algorithmic performance. For instance, a parallel \(7\times 7\) depth separable convolution is added to LMSM method, which reduces mIoU on fish feeding behavior dataset. Similar experiments are performed on the ASPP module. When the original ASPP module is applied in our segmentation task, the algorithm is unable to segment small target objects. Therefore, it is critical to present alternative multi-scale modules for different segmentation tasks, measure which also proves the effectiveness of our proposed method. Moreover, we find that using 3 \(\times\) 3 convolution rather than \(1\times 1\) convolution in the last layer of the LMSM method is more conducive to feature extraction and target segmentation. **SPSA module. ** Table 5 shows the performance comparison between our proposed and the original attention mechanism module. Comparing the original PSA module, we find that the SPSA we proposed obtains competitive performance while ensuring fewer parameters and GFLOPs. This is because the proposed SPSA approach divides groups in the channel dimension, which is very helpful for efforts to reduce the number of parameters. **Ablation study of gradually increasing various modules**. The experimental results presented in Table 6 have shown that applying each strategy separately can improve the score over baseline U-Net. Using the SPSA module can significantly boost the score over the baseline U-Net (improving it from 77.98% to 79.14%). Notably, we also discovered that only adding the LMSM module was able to dramatically improve the results by 1.48% mIoU (improving from 77.98% to 79.46%) when compared to using the SPSA module alone. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Context Module** & **MIoU (\%)** & **Params** & **GFLOPs** \\ \hline PPM & 79.14 & 3.33 M & 94.7 \\ ASPP & 79.29 & 4.51 M & 102.9 \\ \hline LMSM * & 79.38 & 4.54 M & 103.4 \\ LMSM & **79.62** & **2.45 M** & **89.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study of adding LMSM on proposed methods. LMSM *: depth separable convolution is replaced with general convolution; LMSM: proposed method. Figure 12: Improved ASPP module. The goal is to adapt to our fish segmentation task. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attention Module** & **MIoU (\%)** & **Params** & **GFLOPs** \\ \hline PSA & 79.49 & 2.52 M & 97.9 \\ SPSA & **79.62** & **2.45 M** & **89.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study of adding SPSA on proposed methods. Our proposed method, together with SPSA and LMSM methods, yielded 79.52% mIoU. Finally, by adding deep supervision, the score was further slightly improved to 79.62% mIoU. The experimental results demonstrated that the various modules we proposed had obvious performance gains and showed reliable improvements. **Loss function**. Table 7 shows the result of different loss functions on our proposed method. The results show that changing the loss function improves the segmentation score slightly. We can see from the table that RMI achieves the highest score since it utilizes correlation between the pixels. However, the fact that the pixels in the image are interdependent is ignored by cross entropy loss. Thus, RMI is used as the loss function for fish school feeding image segmentation based on this experiment.