Due　to　the　significant　variation　of　municipal　solid　wastes　in　appearance　and　composition,　as　well　as　the　lack　of　abundant　labeled　samples,　deep　learning-based　municipal　solid　waste　detection　is　a　challenging　problem.　This　paper　presents　a　novel　one-shot　municipal　solid　waste　detection　model　based　on　Faster　R-CNN　and　the　attention　mechanism　to　improve　detection　performance　via　object-relevant　feature　enhancement　and　category-level　feature　fusion.　Concretely,　a　spatial　attention-based　feature　enhancement　module,　SAFEM,　is　designed　to　enhance　object-relevant　information　and　improve　object　localization.　Then,　the　channel　attention-based　fusion　module,　CAFM,　is　proposed　and　applied　in　two　stages　separately.　In　the　first　stage,　CAFM　uses　the　category-level　information　of　the　support　features　to　help　the　region　proposal　network　filter　out　non-support　category　query　proposals;　in　the　second　stage,　CAFM　is　used　to　enhance　the　classification　accuracy　of　support　category　objects.　The　effectiveness　of　the　proposed　model　is　verified　by　experiments　on　the　waste　dataset　of　Huawei　Cloud　Competition　2020.　The　experimental　results　demonstrate　that　the　proposed　model　achieves　remarkable　performance　in　one-shot　municipal　solid　waste　detection.　©　2024,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.