# An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images
## 4 Experiments
### Comparison with the State-of-the-Art  
In this section, we compare our result with 15 state-of-the-art segmentation models on test dataset, including SegNet [36], U-Net [37], HRNet, DFN [41], DANet [42], OCNet [43], PSNet [38], DeepLab v3 plus [39], GCN [49], BiSeNet V2 [47], and DDRNet [50], et al. To achieve a fair comparison, the same iterative training strategies were employed to evaluate the effect of the various models to a certain extent. The experimental results show that our proposed approach outperformed other methods and achieved the highest score with 79.62% mIoU. Table 2 presents the result. Namely, our FSFS-Net method with 79.62% mIoU surpassed baseline U-Net and other state-of-the-art methods, which demonstrates the effectiveness of the proposed model. The original U-Net method achieved 77.98% mIoU over the HRNet method, which was in line with our expectations, especially since the U-Net method has been widely used in medical image segmentation and has achieved remarkable results. Figure 11: Sample Images Labeled. The three rows top-bottom presents the marking rules for two target categories, the original image, and the ground truth. The red box and the yellow box, respectively, represent the fish1 and fish2 category. The HRNet, GCN, DeepLab v3 plus, and real-time DDRNet39 methods had similar scores because they use multi-scale fusion to extract contextual semantic information. The HRNet maintained high-resolution representation from all the high- to low-resolution parallel convolutions, which enhance the high-resolution and low-resolution features and achieve multi-scale feature fusion by repeatedly exchanging information between parallel multi-resolution subnets. DeepLab v3 plus utilized encoder-decoder methods and the ASPP module to recover spatial information and to extract multi-scale contextual information. GCN utilized a large convolution kernel to increase the receptive field for addressing classification and locations task simultaneously. DDRNet39 used a deep aggregation pyramid pooling module (DAPPM) to extract contextual information. Despite this, the method we proposed surpassed the abovementioned method since our method combines the advantages of both methods. From Table 2, we can observe that our FSFS-Net method outperforms the method-attention mechanism method (DFN, DANet, and OCNet) and other methods, such as BiSeNet V2, ExFuse, PSPNet and FCN-8s. It is worth mentioning that although the PSNet method achieves a performance that is closer to ExFuse and DFN, its backbone network uses ResNet50 rather than ResNet101. To validate the effect of the backbone network on the algorithm, we used ResNet 50 and ResNet101 as the backbone network of ExFuse and DFN. The experimental results showed that DFN using deeper ResNet101 was favorable to fish segmentation, with a gap of at least 10% mIoU compared to that obtained with a shallow ResNet50 network; however, ExFuse was unaffected by the depth of the backbone network due to the multi-scale fusion module. Additionally, when VGG16 and ResNet152 were applied to DeepLab v3 plus, we discovered that their results were quite similar, proving that the multi-scale fusion could achieve very competitive results without relying on the depth of the feature extraction network. The comprehensive experimental result shows that our method is superior to other methods and can produce more accurate results. The sophisticated HRNet approach has poor generalization performance as compared to our method proposed. The poor performance is primarily due to the fact that it is designed on multi-category feature and large sample datasets. However, our dataset of fish school feeding has a small sample, with