It　is　vital　to　improve　the　performance　of　semantic　segmentation　networks,　since　they　play　an　important　role　in　many　fields,　such　as　self-driving　cars,　medical　image　analysis,　robotic　vision　and　road　traffic　detection.　Some　researchers　combine　object　detection　and　semantic　segmentation　together,　so　that　both　networks　can　enjoy　the　benefits　of　each　other.　However,　their　proposed　networks　involve　retraining,　and　some　traditional　semantic　segmentation　networks　have　yet　to　realize　their　full　potential.　In　this　research,　we　propose　Combined-Net　which　incorporates　existed　object　detection　and　semantic　segmentation　to　improve　the　performance　of　semantic　segmentation　without　resorting　to　retrain.　It　locates　and　revises　the　error　segmentation　based　on　information　of　bounding　box　annotations,　and　hence　improves　mIoU　of　traditional　semantic　segmentation.　The　following　experiment　in　PASCAL　VOC　2012　dataset　shows　that　combining　DeeplabV3+　and　SSD　in　basic　version　of　Combined-Net　improves　the　mIoU　of　original　network　by　4.94%,　while　the　improved　version　brings　the　mIoU　of　this　combination　to　74.37%.　In　best　combination,　Combined-Net　increases　this　metrics　by　11.68%　with　U-Net　and　SSD.　©　2025　SPIE.