Although　image　segmentation　is　widely　applied　in　many　fields　owing　to　the　assistance　of　better　analysis　and　understanding　of　images,　the　models　based　on　fully　convolutional　neural　networks　still　engender　the　problems　of　resolution　reconstruction　and　contextual　information　usage　in　semantic　segmentation.　Aiming　at　the　problems,　a　semantic　propagation　and　fore-background　aware　network　for　image　semantic　segmentation　is　proposed.　A　joint　semantic　propagation　up-sampling　module(JSPU)　is　proposed　to　obtain　semantic　weights　by　extracting　the　global　and　local　semantic　information　from　high-level　features.　Then　the　semantic　information　is　propagated　from　high-level　features　to　low-level　features　for　alleviating　the　semantic　gap　between　them.　The　resolution　reconstruction　is　achieved　through　a　hierarchical　up-sampling　structure.　In　addition,　a　pyramid　fore-background　aware　module　is　proposed　to　extract　foreground　and　background　features　of　different　scales　through　two　parallel　branches.　Multi-scale　fore-background　aware　features　are　captured　by　establishing　the　dependency　relationships　between　the　foreground　and　background　features,　thereby　the　contextual　representation　of　foreground　features　is　enhanced.　Experiments　on　semantic　segmentation　benchmark　datasets　show　that　SPAFBA　is　superior　in　performance.　©　2022,　Science　Press.　All　right　reserved.