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.