# An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images
## 4 Experiments
### Visualizations and Discussion  
In this section, we present visualized segmentation results to evaluate the effectiveness of our method. Additionally, we also discuss the possible limitations of our proposed method in some special cases. The images used for analysis are selected from test samples with simple (feed-before) and hard samples (strong feeding intensity). Finally, a video is tested on the method we proposed to analyze the changes of feeding intensity during the fish school feeding process. Experimental results show that our proposed method can distinguish key features and extract multi-scale features from an image, resulting in the highest score. As shown in Figure 12(a)-f, the method we proposed is effective in segmenting multiple instances in an image. Especially, our method can distinguish two adhesion fish from simple samples. Additionally, our algorithm can correctly segment some challenging instances, such as category confusion, motion blur, and multi-scale features between the same category and different categories. Furthermore, our proposed method can classify some mislabeled instances (label error) into true categories. \begin{table}
\begin{tabular}{c c c c c} \hline
**U-Net** & **SPSA** & **LMSM** & **DS** & **MIoU (\%)** & **Gain** \\ \hline ✓ & & & & 77.98 \\ ✓ & ✓ & & & 79.14 \\ ✓ & ✓ & ✓ & & 79.46 \\ ✓ & ✓ & ✓ & & 79.52 \\ ✓ & ✓ & ✓ & ✓ & **79.62** & \(\uparrow\) 0.1 \\ \hline \end{tabular}
\end{table} 
Table 6: Ablation study of gradually increasing LMSM, SPSA, and the deep supervision (DS) based on baseline U-Net. The experimental results show that the performance can be further improved by using the LMSM and the SPSA strategy. \begin{table}
\begin{tabular}{c c} \hline
**Method** & **MIoU (\%)** \\ \hline Focal loss & 76.75 \\ Weighted cross-entropy & 77.70 \\ LDAM loss & 77.72 \\ OHEM & 77.94 \\ Lovas loss & 79.01 \\ RMI loss & **79.62** \\ \hline \end{tabular}
\end{table} 
Table 7: Different loss functions bring different improvements. Figure 13: Visualized segmentation results on fish school feeding datasets. The result shows that our proposed method can segment multiple instance objects. Examples of fish images are randomly selected from the test dataset. The four column left-to-right represents the input image, the ground truth (Label), the output predicted of FSFS-Net, and the final image. The red rectangles in (**a–f**) rows show that the instances are successfully segmented. The yellow rectangles in (**g–i**,**m**) rows present some segmentation failures. Although our proposed method can recover the spatial information to capture sharper object boundaries (fish with label 1), it still has three limitations. We find that our method designed is ineffective at distinguishing object borders, particularly for fish with the fish2 label, due to the fact that edge pixels tend to be incorrectly classified. In other cases, as seen in Figure 13g-m, there is considerable category confusion. When there is a border adhesion or slight overlap between two fish in a non-feeding image, the algorithm may be unable to clearly distinguish the border of the adhesion area, causing it to misclassify the instances (for example, two fish1 are misclassified as one fish2). In addition, there are clusters and severe occlusions between individual fish in the fish school feeding image (Figure 13m), but the algorithm misclassifies the fish2 category into the fish1 category since boundaries can be distinguished. Finally, to verify the effectiveness of the proposed semantic segmentation method in fish feeding behavior analysis, the FSFS method proposed is tested on a video with 3 min clip. Two indicators (number of pixels and pixel ratio of two semantic categories) are used to distinguish the feeding intensity of fish school. As shown in Figure 14a, we can observe that the number of fish2 semantic pixels is greater than that of fish1, which is mainly due to the large amount of aggregation and occlusion between fish during feeding.