In　recent　years,　the　state-of-the-art　semantic　segmentation　models　have　made　extremely　successful　in　various　challenging　scenes.　However,　the　high　computation　costs　of　these　models　make　it　difficult　to　deploy　to　mobile　devices.　To　better　serve　in　computation　constraint　scenes,　the　semantic　segmentation　model　should　not　only　have　high　segmentation　performance,　but　also　fast　inference　speed.　In　this　paper,　we　proposed　an　efficient　multi-scale　context　module　named　LSPPM,　which　can　gather　abundant　context　information　at　a　low　computation　cost.　Base　on　this,　we　present　a　real-time　semantic　segmentation　model　called　LSPPNet　which　is　specially　designed　for　real-time　application.　We　have　done　an　exhaustive　experiment　to　evaluate　LSPPNet　in　the　challenge　urban　street　scenes　datasets　Cityscapes.　Extensive　experiment　shows　that　LSPPNet　gets　a　better　trade-off　between　segmentation　performance　and　inference　speed.　We　test　LSPPNet　on　an　NVIDIA　2080　super　graphics　card　and　it　can　achieve　75.8%　MIoU　in　Cityscapes　test　set　in　real-time　speed.　©　Published　under　licence　by　IOP　Publishing　Ltd.