# An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images
## 2 Related Work
### Semantic Segmentation  
Deep learning methods have made tremendous progress and have achieved very impressive results in image classification and detection. Despite the success, these methods still face existing challenges in image segmentation, including multi-scale features, receptive field, and low resolution. Among them, the occurrence of multi-scale features is caused by the different objects size between the same or different categories in an image. A small convolution kernel has a small receptive field, which is not conducive to extracting contextual information. While a large convolution kernel can solve the problem of the receptive field, it increases model parameters, meaning that the real-time requirements cannot be met. For resolution issues, low-stage features have more detailed information, while the high-stage features have higher semantic information. Using a downsampling operator increases the receptive field to extract high-level (global/contextual) semantic information, but it reduces the image resolution, making the algorithm unable to accurately locate the individual and extract the object spatial information (object contour and edge information).  
To address the abovementioned challenges, various advanced semantic segmentation algorithms are proposed. SegNet [36] and U-Net [37] adopted encoder-decoder architecture to recover low-level spatial information for keeping a sharper edge. SegNet utilized saved pool indices to recover the reduced spatial information in downsampling. U-Net utilized skip connections to fuse features from different layers. Some straightforward ways to extract multi-scale features include resizing the feature map to different scales using the proposed PSP [38], ASPP [39], and multi-receptive field module (MRFM) [40], as shown in Figure 4. PSPNet and DeeplabV3 use the PSP and ASPP modules, which utilize several pooling with different sizes and multiple parallel atrous convolutions to obtain multi-scale  
Figure 3: Examples image of fish segmentation in previous works. (**A1**): single fish and single category in the simple background; (**A2**): multi-fish and single category in the underwater environment; (**A3**): single fish and multi-category in the simple background; (**A4**): single fish and multi-category, but only single category in an image; (**B1**): multi-fish and multi-category in underwater; (**B2,B3**): single fish in underwater fish school image; (**B4**): multi-fish and multi-category in the complex underwater environment.  
receptive fields, and these modules are located at the end of the backbone. Additionally, the multi-receptive field module (MRFM) [40] is introduced by re-designing the backbone to obtain multi-scale features. Nonetheless, different scales yield features with varying degrees of discrimination, resulting in inconsistency.  
The attention mechanism can capture global dependencies. To distinguish between the boundaries of objects (classification of pixels at edges) and extract key features for feature fusion from different stages, some studies utilized attention mechanisms to extract spatial information and build context dependencies by learning the relationship between pixels. Examples of this approach include DFN [41], DANet [42], OCNet [43], OCRet [44] and other related studies, and they have achieved outstanding results on public datasets. Inspired by them, we propose a multi-scale module to extract features of different scales of target. Subsequently, we propose a shuffle polarized self-attention to extract key features and global dependencies between features.