Facing　complex　remote　sensing　scenes,　detection　models　with　single　detection　mechanisms　cannot　always　provide　satisfactory　detection　capabilities.　In　order　to　obtain　better　detection　performance　in　various　remote　sensing　scenes,　this　paper　constructs　a　novel　ensemble　model,　namely:　the　multiple　prediction　mechanisms　ensemble　(MPME).　In　order　to　improve　the　feature　representation　ability　and　region　recognition　ability　of　the　ensemble　model,　we　build　the　ensemble　of　feature　pyramids　(EFP)　and　the　ensemble　of　detection　heads　(EDH)　respectively.　In　order　to　further　improve　the　detection　accuracy　of　the　ensemble　model,　we　propose　a　training　strategy　(k-Nearest　Loss　Learning),　so　that　each　sub-detector　does　not　need　to　learn　a　trade-off　among　all　training　samples,　and　also　reduces　the　possibility　of　model　over-fitting.　The　experimental　results　show　that　our　MPME　is　a　more　efficient　and　effective　ensemble　model.　Compared　with　other　ensemble　models,　our　MPME　has　a　faster　detection　speed　and　better　detection　accuracy.　Compared　with　other　state-of-the-art　detectors,　our　detector　also　achieves　superior　detection　performance.