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 ...
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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.
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