The　timely　detection　and　rapid　disposal　of　forest　fires　are　the　key　to　protecting　the　ecological　environment.　However,　in　the　detection　of　forest　fires　in　the　aerial　photography　scene　of　UAVs,　due　to　factors　such　as　small　fire　points　in　aerial　photography,　large　influence　of　smoke,　and　complex　aerial　photography　background,　traditional　deep　learning　fire　detection　algorithms　often　have　the　problem　of　low　detection　accuracy.　To　solve　this　problem,　this　paper　proposes　an　aerial　forest　fire　detection　method　based　on　transfer　learning　and　improved　Faster　RCNN　algorithm.　First　of　all,　in　view　of　the　lack　of　forest　fire　data　from　the　existing　UAV　aerial　photography,　we　use　transfer　learning　technology　to　use　the　convolutional　neural　network　parameters　trained　on　the　ImageNet　dataset　to　initialize　the　convolutional　layer　of　Faster　RCNN.　Second,　Faster　RCNN　is　improved　based　on　feature　fusion　and　attention　mechanism.　We　introduced　the　feature　fusion　structure　PPM　to　improve　the　receptive　field　of　the　model,　thereby　improving　the　detection　accuracy　of　aerial　forest　fires.　In　addition,　the　attention　mechanism　is　used　to　replace　the　original　Flatten　layer　and　FC1　and　FC2　fully　connected　layers　of　Faster　RCNN　to　further　improve　the　learning　efficiency　and　detection　accuracy　of　the　model.　The　experimental　results　show　that　our　method　achieves　a　detection　accuracy　of　93.7%　when　detecting　forest　fires　in　UAV　aerial　images,　which　is　more　than　18.0%　higher　than　the　traditional　method,　and　meets　the　real-time　detection　requirements.　This　shows　that　our　method　can　be　effectively　applied　to　forest　fire　detection　in　UAV　aerial　photography　scenarios,　and　is　expected　to　play　an　important　role　in　actual　forest　fire　detection.　©　2023　IEEE.