The　monocular　3D　object　detection　methods　based　on　Transformer　have　recently　progressed　significantly.　However,　most　existing　methods　struggle　to　effectively　handle　fine-grained　objects　and　complex　scenes,　particularly　when　capturing　the　features　of　occluded　or　small　objects.　To　tackle　these　issues,　we　propose　a　monocular　3D　object　detector,　CU-DETR,　based　on　the　MonoDETR　framework.　CU-DETR　introduces　the　local-global　fusion　encoder　to　enhance　local　feature　extraction　and　fusion　and　applies　an　uncertainty　perturbation　strategy　in　position　encoding　to　enhance　the　model’s　performance　in　handling　complex　scenes.　Experimental　results　on　the　KITTI　public　dataset　demonstrate　that　CU-DETR　outperforms　the　MonoDETR.　©　2024　SPIE.