Forest　fires　may　cause　serious　human　and　economic　losses,　and　it　is　also　a　key　technology　linked　to　realizing　forest　intelligent　fire　detection　and　prevention.　Therefore,　the　study　of　forest　fire　detection　and　early　warning　system　has　great　social　application　value.　However,　the　traditional　methods　of　detecting　fires　have　weak　real-time　and　detection　capabilities.　The　new　generation　of　deep　learning　technology,　especially　convolutional　neural　networks,　provides　new　means　and　methods　for　fire　detection.　The　convolutional　neural　network　has　the　characteristics　of　good　fault　tolerance,　self-adaptability,　self-learning　ability,　and　weight　sharing,　which　makes　the　convolutional　neural　network　can　identify　fires　in　a　working　principle　close　to　that　of　the　human　eye.　The　fire　detection　method　based　on　deep　learning　has　high　accuracy　and　robustness.　However,　due　to　its　large　number　of　parameters　and　a　large　number　of　calculations,　it　is　difficult　to　be　practically　applied.　With　the　development　of　edge　computing,　it　is　now　possible　to　use　the　edge　instead　of　the　cloud　for　computing,　which　will　greatly　reduce　latency.　This　paper　collects　a　large　number　of　forest　fire　images　as　a　data　set,　trains　a　model　that　can　recognize　forest　fires　based　on　the　YOLOv7　network,　and　further　deploys　the　trained　model　to　the　edge　server　RK3588.　Finally,　a　front-end　visual　interface　capable　of　displaying　fire　recognition　results　was　built　using　Pyqt5.　The　model　in　this　method　has　good　robustness　and　generalization　ability,　and　the　application　of　edge　computing　in　fire　forest　detection　further　improves　the　speed　of　fire　detection,　which　provides　a　new　way　for　forest　fire　detection.　©　2023　IEEE.