Unmanned　aerial　vehicle　(UAV)　remote-sensing　images　have　a　wide　range　of　applications　in　wildfire　monitoring,　providing　invaluable　data　for　early　detection　and　effective　management.　This　paper　proposes　an　improved　few-shot　target　detection　algorithm　tailored　specifically　for　wildfire　detection.　The　quality　of　UAV　remote-sensing　images　is　significantly　improved　by　utilizing　image　enhancement　techniques　such　as　Gamma　change　and　Wiener　filter,　thereby　enhancing　the　accuracy　of　the　detection　model.　Additionally,　ConvNeXt-ECA　is　used　to　focus　on　valid　information　within　the　images,　which　is　an　improvement　of　ConvNeXt　with　the　addition　of　the　ECANet　attention　mechanism.　Furthermore,　multi-scale　feature　fusion　is　performed　by　adding　a　feature　pyramid　network　(FPN)　to　optimize　the　extracted　small　target　features.　The　experimental　results　demonstrate　that　the　improved　algorithm　achieves　a　detection　accuracy　of　93.2%,　surpassing　Faster　R-CNN　by　6.6%.　Moreover,　the　improved　algorithm　outperforms　other　target　detection　algorithms　YOLOv8,　RT-DETR,　YoloX,　and　SSD　by　3.4%,　6.4%,　7.6%　and　21.1%　respectively.　This　highlights　its　superior　recognition　accuracy　and　robustness　in　wildfire　detection　tasks.　©　2024　–　IOS　Press.　All　rights　reserved