The sustainable development of marine fisheries depends on the accurate measurement of data on fish stocks. Semantic segmentation methods based on deep learning can be applied to automatically obtain segmentation masks of fish in images to obtain measurement data. However, general semantic segmentation methods cannot accurately segment fish objects in underwater images. In this study, a Dual Pooling-aggregated Attention Network (DPANet) to adaptively capture long-range dependencies through an efficient and computing-friendly manner to enhance feature representation and improve segmentation performance is proposed. Specifically, a novel pooling-aggregate position attention module and a poolingaggregate channel attention module are designed to aggregate contexts in the spatial dimension and channel dimension, respectively. These two modules adopt pooling operations along the channel dimension and along the spatial dimension to aggregate information, respectively, thus reducing computational costs. In these modules, attention maps are generated by four different paths and are aggregated into one. The authors conduct extensive experiments to validate the effectiveness of the DPANet and achieve new state-ofthe-art segmentation performance on the well-known fish image dataset DeepFish as well as on the underwater image dataset SUIM, achieving a Mean IoU score of 91.08% and 85.39% respectively, while significantly reducing FLOPs of attention modules by about 93%.

The technology for autonomous navigation on inland waterways is worth investigating, and navigable water surface segmentation is a key part of this technology. Semantic segmentation methods based on deep learning are able to distinguish between water surface areas and non-water surface areas. However, existing semantic segmentation methods cannot meet the requirements of the water surface segmentation task in terms of both segmentation precision and real-time performance. In this study, a Swap Attention Bilateral Segmentation Network (SA-BiSeNet) is proposed to improve segmentation performance while ensuring model inference speed by better fusing the two features of the dual-branch down-sampling network using the attention mechanism. Specifically, an innovative Swap Attention Module is designed to model the dependency between the features of the spatial detail branch and the features of the semantic branches, thus expanding the receptive fields of the spatial detail and semantic branches to each other's global contexts. This design can effectively fuse features and thus enhance feature representation. Experiments were conducted on the inland waterway dataset USVInland to verify the performance of SA-BiSeNet in terms of segmentation precision and inference speed, and SA-BiSeNet achieved 93.65% Mean IoU and maintained the same level of fps as the baseline.