The　proliferation　of　deep　learning　has　driven　research　into　deep　learning-based　logo　detection,　which　usually　needs　a　large　number　of　annotated　data　to　train　the　model.　However,　due　to　the　occasional　appearance　of　new　brands　or　the　high　cost　of　annotation,　the　number　of　training　data　is　limited.　Against　this　backdrop,　the　authors　adapt　the　few-shot　object　detection　into　logo　detection,　and　thus　present　a　cutting-edge　method　called　Double　Classification　Head　(DCH)　for　Few-Shot　Logo　Detection　(DCH-FSLogo),　which　aims　at　detecting　the　unseen　logo　classes　using　few　annotated　data.　Unlike　the　traditional　few-shot　detection,　some　logo　objects　are　similar　to　their　backgrounds　and　have　diverse　shapes　as　well.　For　this　reason,　the　authors　adopt　balanced　feature　pyramid　and　deformable　Region　of　Interest　pooling　in　DCH-FSLogo,　this　enhances　the　feature　extraction　capability　and　adapts　to　the　different　logo　shapes.　In　addition,　we　introduce　the　DCH　for　few-shot　logo　detection　to　detect　logo　objects　using　few　annotated　data.　Specifically,　we　use　an　extra　classification　head　for　the　base　classes　to　ease　the　influence　from　the　novel　classes.　The　experimental　results　on　four　datasets,　namely:　FlickrLogos-32,　FoodLogoDet-1500-100,　LogoDet-3K-100　and　QMUL-OpenLogo-100,　demonstrate　that　our　method　achieves　better　performance.