With　the　increasing　number　of　augmented　reality　apps　for　houses　in　recent　years,　home　modeling　is　essential　to　complete　a　3D　reconstruction　via　identifying　the　primary　features　of　the　house　based　on　a　2D　floorplan.　Due　to　the　dispersed　wall　arrangement　in　2D　floor　layouts　and　the　abundant　interference　information　surrounding　varied　thicknesses,　existing　segmentation　methods　mainly　rely　on　image　morphology　or　use　deep　learning　models　in　other　fields　like　Unet.　However,　these　schemes　do　not　solve　poor　robust　performance　problems.　In　this　paper,　we　propose　an　Reflect　Strip　Pooling　Unet　(RSP-Unet)　to　enhance　the　segmentation　capabilities　of　the　network　for　strip　features.　Specifically,　we　utilize　reflect　strip　pooling　to　replace　the　maximum　pooling　step　and　reduce　feature　loss　during　the　downsampling　in　the　Unet　network.　More　importantly,　the　proposed　module　is　also　integrated　with　the　SE　(Squeeze-and-　Excitation)　mechanism　to　interact　with　input　from　several　channels,　lessen　model　overfitting,　and　increase　model　robustness.　Finally,　our　extensive　experience　shows　that　the　results　on　the　self-built　floorplan　dataset　demonstrate　that　the　mean　Intersection　Over　Union(mIOU)　is　increased　by　8.34%　and　the　Dice　coefficient　is　increased　by　8.78%　compared　with　the　original　Unet　model.　©　2023,　John　Wiley　and　Sons　Inc.　All　rights　reserved.