With　the　rapid　development　of　artificial　intelligence　and　Unmanned　Aerial　Vehicle　(UAV)　technology,　AI-based　UAVs　are　increasingly　utilized　in　various　industrial　and　civilian　applications.　This　paper　presents　a　distributed　Edge-Cloud　collaborative　framework　for　UAV　object　detection,　aiming　to　achieve　real-time　and　accurate　detection　of　ground　moving　targets.　The　framework　incorporates　an　Edge-Embedded　Lightweight　(${{\text{E}}＜^＞{2}}\text{L}$E2L)　object　algorithm　with　an　attention　mechanism,　enabling　real-time　object　detection　on　edge-side　embedded　devices　while　maintaining　high　accuracy.　Additionally,　a　decision-making　mechanism　based　on　fuzzy　neural　network　facilitates　adaptive　task　allocation　between　the　edge-side　and　cloud-side.　Experimental　results　demonstrate　the　improved　running　rate　of　the　proposed　algorithm　compared　to　YOLOv4　on　the　edge-side　NVIDIA　Jetson　Xavier　NX,　and　the　superior　performance　of　the　distributed　Edge-Cloud　collaborative　framework　over　traditional　edge　computing　or　cloud　computing　algorithms　in　terms　of　speed　and　accuracy