# An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images
## 1 Introduction  
Intelligent feeding is crucial in intensive aquaculture systems for optimizing resource usage and promoting fish development and well-being while minimizing environmental consequences. Image analysis technologies have emerged as powerful tools in achieving precision aquaculture and have shown potential in image-based tasks such as fish identification [1], detection [2], tracking and behavior analysis [3]. Using image technologies to analyze fish behavior enables us to provide important information to farmers for their use in making feeding decisions. In general, image techniques based on tracking are used to extract fish trajectories and then utilize extracted characteristic from trajectories to quantify individual fish feeding behavior [4; 5; 6; 7]. However, these methods are not suitable for intensive aquaculture since fishes are generally managed at the group level, and motion of fish school is complex [8]. Recently, fish school feeding behavior analysis hasappeared as a competitive alternative to individual behavior analysis [9; 10; 11; 12; 13; 14]. For the quantification analysis of feeding behavior, one of the most challenging problems is automatic fish segmentation. Thus, it is essential to obtain a precise, reliable, and standardized fish segmentation approach, surpassing a trained human-level performance, which can provide valuable assistance to farmers. Several segmentation methods have been widely applied in fish size measurement, counting, and species identification. Traditional segmentation methods, including background models [15] and contour-based [16] and color-based [17; 18] models, focus on segmenting single-category objects and only segment the objects in an image into foreground and background. However, the traditional methods do not accurately segment the object into multiple categories in the case of images in complex scenes. Moreover, there are still significant challenges to the segmentation of individual fish from fish school feeding images due to motion blur, adhesion, occlusion, overlap, and aggregation between individual fish, as shown in Figure 1b-d, ultimately leading to incorrect behavior analysis. The characteristics vary considerably between feeding image and non-feeding image, resulting in the algorithm designed on the non-feeding image not being well generalized to the feeding image. The semantic segmentation approach proved to be excellent for addressing multi-category object segmentation problems in an image [19]. It is a pixel-wise classification technique, with differences from other classification and detection methods. Its main task is assigning each pixel from the input image with a semantic label and aims to segment the instances of interest. With the popularization of data-driven deep learning techniques, the use of semantic segmentation has increased dramatically in image analysis tasks and demonstrated promising results in empirical tasks such as autonomous driving [20], and medical diagnosis [21; 22]. Despite the significant progress achieved, the most existing SOTA semantic methods are not directly applicable since the size of objects and categories in an image are diverse. Therefore, the algorithm needs to be redesigned for application to different segmentation tasks. On the other hand, deep learning algorithms have shown great potential for automatic fish identification, detection, and individual tracking, but there are few studies to have quantified fish school feeding behavior by using a semantic segmentation algorithm. Fish semantic segmentation is a challenging endeavor that faces two major obstacles: it is multi-scale and there are similarities between fish. (1) In terms of multiple scales between the same categories and different categories: as shown in Figure 2, for different categories, one is small in scale, and the other has a large size difference. In addition, there are existing scale differences between the same categories. Therefore, multi-scale objects require the model to have multi-scale capture capabilities.