# An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images
## 1 Introduction
(2) The similarity between fish causes category confusion due to occlusion and overlapping between the individual fish, which may lead to low segmentation accuracy. Moreover, the similarity of colors between  
Figure 1: Existing dataset examples with different feeding states. These images show difference between feeding and non-feeding image states. There is motion blur, adhesion, occlusion, overlap, and clustering in the feeding image. (**a**) Non-feeding; (**b**) moderate feeding; (**c**) weak feeding; (**d**) strong feeding. individual fish and the blurring of object edges make it difficult to depict fish boundaries and classify them into correct categories for the pixels close to or at the boundaries of fish due to the intrinsic weakness of convolutional networks. The above factors bring serious segmentation challenges and make it difficult to generalize the model. To solve the above problems, we use a semantic segmentation algorithm to quantify fish school feeding behavior in a high-density scene. In this paper, we propose the use of an FSFS-Net method to achieve the segmentation of two-category objects (labeled rules for semantic categories are detailed in Section 4.1) in the feeding process. To solve the problems of similarity, we propose the use of shuffle polarized self-attention (SPSA) methods to learn dependencies between features in images. Furthermore, considering the trade-off between speed and accuracy performance, we also propose a lightweight multi-scale module (LMSM) to extract multi-scale features and more high-level semantic features by using a different the receptive field which does not introduce computation overhead for inference. Finally, we conduct comprehensive experiments to evaluate the effectiveness of the algorithm. The experimental results show that our proposed method achieves the competitive performance with a 79.62 mean IoU without dataset augmentation of fish feeding dataset, and that it outperforms other segmentation algorithms, such as U-Net, SegNet, FCN, DeepLab v3 plus, GCN, HRNet-w48, DDRNet, LinkNet, BiSeNet v2, DANet and CCNet. To the best of our knowledge, the superior performance of our proposed method over the state-of-the-art method is mainly due to the proposed SPSA and LMSM module. The result and analysis of comparative experiments prove that our method is straightforward and effective, and that it can be used to quantify the feeding intensity of a fish school. Our contributions can be summarized as follows:  
* An annotated dataset is presented for occluded and aggregated fish segmentation from feeding images. The dataset contains 1038 training and 323 testing images, as well as their ground truth semantic labels. The 1361 images marked have 72,537 instances with the fish1 category and 15,925 instances with fish2 category, and the background pixel accounts for a relatively large amount in an image. * This study demonstrates that semantic segmentation is employed in fish school feeding behavior quantification. Particularly, we propose using a FSFS-Net method, equipped with a simple and effective module, to extract feature dependencies and multi-scale features from an image. * A SPSA module is proposed to learn dependencies between features, which can alleviate color and size similarity. Additionally, a novel LMSM is developed to fuse multi-scale features by using different receptive fields to address the multi-scale problem, which is helpful in the task of segmentation. * The proposed approach achieves a competitive performance with a mean IoU of 79.62% while ensuring computational efficiency over other semantic segmentation algorithms for building fish feeding datasets. Further, the proposed approach is tested on a video with 3 min clip to analyze the feeding intensity of fish. The promising results demonstrate the superiority of our proposed algorithm, the use of which would promote fish segmentation and behavior analysis research in aquaculture. Figure 2: Multi-scale in the fish feeding image.