In　response　to　the　problem　of　the　large　parameter　size　of　semantic　segmentation　networks,　making　it　difficult　to　deploy　on　memory-constrained　edge　devices,　a　lightweight　real-time　semantic　segmentation　algorithm　is　proposed　based　on　BiLevelNet.　Firstly,　dilated　convolutions　are　employed　to　augment　the　receptive　field,　and　feature　reuse　strategies　are　integrated　to　enhance　the　network＇s　region　awareness.　Next,　a　two-stage　PBRA　(Partial　Bi-Level　Route　Attention)　mechanism　is　incorporated　to　establish　dependencies　between　distant　objects,　thereby　augmenting　the　network＇s　global　perception　capability.　Finally,　the　FADE　operator　is　introduced　to　combine　shallow　features　to　improve　the　effectiveness　of　image　upsampling.　Experimental　results　show　that,　at　an　input　image　resolution　of　512×1024,　the　proposed　network　achieves　an　average　Intersection　over　Union　(IoU)　of　75.1%　on　the　Cityscapes　dataset　at　a　speed　of　121　frames　per　second,　with　a　model　size　of　only　0.7　M.　Additionally,　at　an　input　image　resolution　of　360×480,　the　network　achieves　an　average　IoU　of　68.2%　on　the　CamVid　dataset.　Compared　with　other　real-time　semantic　segmentation　methods,　this　network　achieves　a　balance　between　speed　and　accuracy,　meeting　the　real-time　requirements　for　applications　like　autonomous　driving.　©　2024　Chinese　Academy　of　Sciences.　All　rights　reserved.