Deep　learning-based　intelligent　waste　detection　has　been　appealing　and　promising　for　resource　conservation　and　environmental　preservation.　However,　collecting　and　labeling　numerous　samples　to　train　waste　detection　models　is　time-consuming　and　labor-intensive.　This　paper　proposes　a　few-shot　waste　detection　model　based　on　a　dual　attention　mechanism　and　Dynamic　Hard　Sample　(DHS)　triplet　loss,　named　DHS-FSOD,　which　can　effectively　recognize　and　locate　a　new　waste　category　object　only　with　fine-tuning　on　a　few　annotated　samples.　First,　the　DHS-FSOD　model　used　deformable　convolution　in　the　feature　extraction　network　to　improve　the　detection　performance　for　the　same　category　of　objects　with　different　morphology.　Then,　a　dual　attention　module　was　designed　to　help　the　RPN　network　improve　the　quality　of　proposals　and　reduce　the　error　classification　rate　by　filtering　out　the　feature　information　irrelevant　to　the　object　category　in　the　query　sample.　Furthermore,　the　DHS　triplet　loss　was　proposed　to　improve　the　model＇s　ability　to　distinguish　wastes　with　similar　appearances　but　different　classes.　The　effectiveness　of　DHS-FSOD　was　verified　by　ablation　and　comparison　experiments　on　the　MS　COCO　and　Huawei　waste　datasets.