Early　detection　of　colorectal　polyps　is　of　great　significance　in　preventing　colorectal　cancer.　However,　existing　segmentation　methods　often　struggle　to　balance　accuracy　and　computational　efficiency.　To　address　this　issue,　this　paper　proposes　a　lightweight　and　efficient　polyp　segmentation　network　named　Lite-PolypNet.　Built　upon　MobileNetV3　as　the　backbone,　the　network　integrates　a　progressive　feature　aggregation　module,　a　global　attention　augmentation　module,　and　a　dual-branch　decoder　structure　to　effectively　fuse　multi-scale　features　and　global　contextual　information,　thereby　enhancing　boundary　reconstruction　and　small　polyp　detection　capabilities.　Extensive　experiments　conducted　on　five　public　datasets　demonstrate　that　Lite-PolypNet　achieves　high　segmentation　accuracy　(with　a　maximum　Dice　score　of　94.7%)　while　significantly　reducing　model　parameters　and　computational　complexity.　Compared　with　representative　baseline　models,　Lite-PolypNet　reduces　the　number　of　parameters　by　a　factor　of　more　than　six　and　significantly　decreases　the　FLOPs,　making　it　suitable　for　deployment　in　resource-constrained　environments.