# An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images
## 4 Experiments
### Ablation Study  
To further verify our contributions, we conducted an extensive ablation study on the fish feeding test dataset. We took the basic U-Net network as the baseline, and gradually designed the strategies in this paper, such as loss function, PSPA, LMSM, and deep supervision. Our experiments demonstrated that increasing LMSM and SPSA further improved the score over the baseline model. To validate the performance of our proposed module, our method was compared with previous ASPP, PPM, and improved U-Net benchmarks. In addition, the various loss functions were also used for comparison, such as focal loss, weighted cross-entropy, LDAM loss, OHEM, Lovas loss, and RMI loss. **Baseline**. Based on comparative experiments (the detailed results are shown in Section 4.3), we observed that baseline U-Net achieves the best results compared with other methods. Therefore, we selected U-Net architecture for further exploration on the fish school feeding dataset. The result in Table 3 presents the influence of data augmentation, the number of downsamplings, and the number of skip connections. We find that increasing the number of downsamplings to 5 improves mIoU from 77.98% to 78.97% without dataset augmentation, demonstrating that expanding the receptive is beneficial to our fish segmentation task. However, the number of skip connections is 5, and mIoU decreases from 78.97% to 78.8%. We also find that using data augmentation does not lead to significant improvements in the U-Net model (only improving by 0.17). U-Net+ (the number of downsamplings is 5) with dataset augmentation achieves 79.29 mIoU, but our proposed FSFS-Net method achieves a high score with 79.62% mIoU and 2.45 M parameters. Thus, our approach outperforms the UNet and U-Net+ methods in terms of both segmentation performance (the mIoU score) and computation costs (the number of parameters). According to the ablation study based on U-Net+, increasing the number of skip connection not only increases model calculations but also reduces algorithmic performance. Furthermore, data augmentation makes no discernible contribution to the improvement of algorithmic performance. Therefore, we use four skip connections without data augmentation in the following experiments. **Multi-scale module**. Table 4 shows the performance comparison and reports the parameters contained by the LMSM, PPM, and ASPP methods. Comparing FSFS-Net with the ASPP and PPM method, we observe that the LMSM method we proposed brings a considerable improvements and obtains the best result with fewer parameters and GFLOPs. Since the proposed LMSM approach is a straightforward implementation for learning contextual semantic information by expanding receptive field and extracting multi-scale features, the FSFS-Net with PPM and ASPP achieves 79.14% and 79.29% mIoU, respectively. When depth separable convolution of our proposed method proposed is replaced with general convolution, the algorithm achieves 79.38 mIoU, while their overall learnable \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **DA** & **NSC** & **MIoU (\%)** & **Params** & **GFLOPs** \\ \hline \multirow{2}{*}{U-Net} & W/O & 4 & 77.98 & 3.45 M & 166.2 \\  & W & 4 & 78.15 & 3.45 M & 166.2 \\ \hline \multirow{2}{*}{U-Net+} & W/O & 5 & 78.80 & 5.35 M & 99.1 \\  & W/O & 4 & 78.97 & 2.99 M & 89.3 \\  & W & 4 & 79.29 & 2.99 M & **89.3** \\ \hline \multirow{2}{*}{FSFS-Net} & W & 4 & 79.37 & **2.45 M** & 89.6 \\  & W/O & 4 & **79.62** & **2.45 M** & 89.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparisons of our proposed and baseline U-Net method. U-Net+ shows that the U-Net model uses five down-sampling. DA: data augmentation; NSC: number of skip connections. parameters are higher than those of the original LMSM method. It is worth noting that we alter the ASPP module to adapt to our dataset since only two categories need to be segmented for our segmentation task, as shown in Figure 12. The PPM method is consistent with the literature.