Real-time　object　detection　on　embedded　unmanned　aerial　vehicles　(UAVs)　is　crucial　for　emergency　rescue,　autonomous　driving,　and　target　tracking　applications.　However,　UAVs＇　hardware　limitations　create　conflicts　between　model　size　and　detection　accuracy.　Moreover,　challenges　such　as　complex　backgrounds　from　the　UAV＇s　perspective,　severe　occlusion,　densely　packed　small　targets,　and　uneven　lighting　conditions　complicate　real-time　detection　for　embedded　UAVs.　To　tackle　these　challenges,　we　propose　AUHF-DETR,　an　embedded　detection　model　derived　from　RT-DETR.　In　the　backbone,　we　introduce　a　novel　WTC-AdaResNet　paradigm　that　utilizes　reversible　connections　to　decouple　small-object　features.　We　further　replace　the　original　global　attention　mechanism　with　the　PSA　module　to　strengthen　inter-feature　relationships　within　each　ROI,　thereby　resolving　the　embedded　challenges　posed　by　RT-DETR＇s　complex　token　computations.　In　the　encoder,　we　introduce　a　BDFPN　for　multi-scale　feature　fusion,　effectively　mitigating　the　small-object　detection　difficulties　caused　by　the　baseline＇s　Hungarian　assignment.　Extensive　experiments　on　the　public　VisDrone2019,　HIT-UAV,　and　CARPK　datasets　demonstrate　that　compared　with　RT-DETR-r18,　AUHF-DETR　achieves　a　2.1%　increase　in　APs　on　VisDrone2019,　reduces　the　parameter　count　by　49.0%,　and　attains　68　FPS　(AGX　Xavier),　thus　satisfying　the　real-time　requirements　for　small-object　detection　in　embedded　UAVs.