Object　detection　in　unmanned　aerial　vehicle　(UAV)　images　poses　significant　challenges　due　to　complex　scale　variations　and　class　imbalance　among　objects.　Existing　methods　often　address　these　challenges　separately,　overlooking　the　intricate　nature　of　UAV　images　and　the　potential　synergy　between　them.　In　response,　this　paper　proposes　AD-Det,　a　novel　framework　employing　a　coherent　coarse-to-fine　strategy　that　seamlessly　integrates　two　pivotal　components:　adaptive　small　object　enhancement　(ASOE)　and　dynamic　class-balanced　copy–paste　(DCC).　ASOE　utilizes　a　high-resolution　feature　map　to　identify　and　cluster　regions　containing　small　objects.　These　regions　are　subsequently　enlarged　and　processed　by　a　fine-grained　detector.　On　the　other　hand,　DCC　conducts　object-level　resampling　by　dynamically　pasting　tail　classes　around　the　cluster　centers　obtained　by　ASOE,　maintaining　a　dynamic　memory　bank　for　each　tail　class.　This　approach　enables　AD-Det　to　not　only　extract　regions　with　small　objects　for　precise　detection　but　also　dynamically　perform　reasonable　resampling　for　tail-class　objects.　Consequently,　AD-Det　enhances　the　overall　detection　performance　by　addressing　the　challenges　of　scale　variations　and　class　imbalance　in　UAV　images　through　a　synergistic　and　adaptive　framework.　We　extensively　evaluate　our　approach　on　two　public　datasets,　i.e.,　VisDrone　and　UAVDT,　and　demonstrate　that　AD-Det　significantly　outperforms　existing　competitive　alternatives.　Notably,　AD-Det　achieves　a　(Formula　presented.)　average　precision　(AP)　on　the　VisDrone　dataset,　surpassing　its　counterparts　by　at　least　(Formula　presented.).　©　2025　by　the　authors.