Colon　polyps　in　colonoscopy　images　exhibit　significant　differences　in　color,　size,　shape,　appearance,　and　location,　posing　significant　challenges　to　accurate　polyp　segmentation.　In　this　paper,　a　Weighted　Dual-branch　Feature　Fusion　Network　is　proposed　for　Polyp　Segmentation,　named　WDFF-Net,　which　adopts　HarDNet68　as　the　backbone　network.　Firstly,　a　dual-branch　feature　fusion　network　architecture　is　constructed,　which　includes　a　shared　feature　extractor　and　two　feature　fusion　branches,　i.e.　Progressive　Feature　Fusion　(PFF)　branch　and　Scale-aware　Feature　Fusion　(SFF)　branch.　The　branches　fuse　the　deep　features　of　multiple　layers　for　different　purposes　and　with　different　fusion　ways.　The　PFF　branch　is　to　address　the　under-segmentation　or　over-segmentation　problems　of　flat　polyps　with　low-edge　contrast　by　iteratively　fusing　the　features　from　low,　medium,　and　high　layers.　The　SFF　branch　is　to　tackle　the　the　problem　of　drastic　variations　in　polyp　size　and　shape,　especially　the　missed　segmentation　problem　for　small　polyps.　These　two　branches　are　complementary　and　play　different　roles,　in　improving　segmentation　accuracy.　Secondly,　an　Object-aware　Attention　Mechanism　(OAM)　is　proposed　to　enhance　the　features　of　the　target　regions　and　suppress　those　of　the　background　regions,　to　interfere　with　the　segmentation　performance.　Thirdly,　a　weighted　dual-branch　the　segmentation　loss　function　is　specifically　designed,　which　dynamically　assigns　the　weight　factors　of　the　loss　functions　for　two　branches　to　optimize　their　collaborative　training.　Experimental　results　on　five　public　colon　polyp　datasets　demonstrate　that,　the　proposed　WDFF-Net　can　achieve　a　superior　segmentation　performance　with　lower　model　complexity　and　faster　inference　speed,　while　maintaining　good　generalization　ability.　IEEE