# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 4 Discussion  
The results in Tables 2 and 3 show that the segmentation scheme combined with the ensemble learning strategy outperforms other existing algorithms in terms of mIoU and pixel accuracy compared to segmentation using only the Swin Transformer algorithm. It can also be seen from Tables 2 and 3 that the segmentation network proposed in this paper is less accurate for small targets than large targets. On the one hand, there may be fewer images containing small targets in the dataset, which leads to the model being biased towards larger targets during training; on the other hand, the area of small targets is so  
Figure 10: The breeding situation of different ports. (**a**) Xincun Port (2017); (**b**) 2018; (**c**) 2019; (**d**) 2020. (**e**) Potou Port (2017); (**f**) 2018; (**g**) 2019; (**h**) 2020. (**i**) Li’an Port (2017); (**j**) 2018; (**k**) 2019; (**l**) 2020.  
Figure 9: The results of test. (**a**) Potou Port; (**b**) Segmentation; (**c**) Visualization; (**d**) Xincun Port; (**e**) Segmentation; (**f**) Visualization; (**g**) Li’an Port; (**h**) Segmentation; (**i**) Visualization.  
small that there are fewer anchors containing them, which also means that the probability of small targets being detected becomes smaller. It can be seen from Figure 9 that the segmentation scheme proposed in this paper has a good segmentation effect, but there are still some cages that have not been segmented in the segmentation result graph, as shown in Figure 11. The image block on the corner of the image is an enlarged view of the corresponding area. First, the farming cages in this part have similar characteristics to the abandoned cages, and there is no obvious farming mouth; otherwise, the cages in this area are built in a different way, which does not appear in the training set, resulting in the model not learning the features.  
Considering that the scale of aquaculture varies in different seasons and harsh weather may result in unclear remote sensing images, the experimental pictures are selected from the conditions of the peak season of aquaculture and good weather. The result in Figure 8 shows that the breeding density of each port reached the maximum in 2019, and then gradually decreased, and even Li'an Port will no longer carry out cage culture in 2021. The breeding density of Potou Port also surpassed that of Xincun Port in 2018, ranking first among the three ports. The breeding density of Li'an Port has always remained the lowest. Through efficient statistics on the breeding density of cages in different ports, effective macro-control can be achieved to prevent the unreasonable layout of breeding cages.  
Using deep learning technology to count the distribution density of cages, on the one hand, can greatly improve the success rate of nursery and adult fish breeding, and reduce the possibility of ecological pollution; on the other hand, the introduction of deep learning technology is conducive to the establishment of a new modern smart breeding model. In large-scale and high-density aquaculture with cluster effect, efficient and scientific data analysis can reduce the input of human and material resources. At the same time, it can also reduce the loss caused by human subjective judgment errors and lag, thereby improving the economic benefits of aquaculture. From the perspective of maintaining the ecosystem, the introduction of deep learning technology is conducive to the rational and scientific layout of the marine aquaculture industry, promotes the establishment of green and healthy styles of marine aquaculture, and protects the ecological environment in harbour areas. This innovative style plays a significant role in promoting the development of environment-friendly aquaculture, reducing the use of drugs, reducing the breeding of pathogens and realizing pollution-free aquaculture, and can provide strong technical support for the sustainable development of marine aquaculture.  
Figure 11: Visualization of the result.