In　order　to　improve　the　efficiency　of　post-disaster　treatment　of　power　distribution　network,　the　application　of　UAV　in　disaster　reduction　and　relief　has　been　paid　much　attention　by　the　power　sector.　Aiming　at　the　loss　assessment　needs　of　overhead　transmission　lines　in　distribution　network,　this　paper　proposes　an　innovative　solution　of　pole　detection　and　counting　in　distribution　network　based　on　UAV　inspection　line　video.　Combined　with　the　characteristics　of　YOLO’s　rapid　detection,　the　convolution　neural　network　is　applied　to　the　image　detection　of　the　pole　state.　In　addition,　the　pole　data　and　corresponding　images　are　obtained　at　the　same　time　of　detecting　the　inspection　line　video.　Therefore,　the　power　department　can　quickly　count　the　losses　to　cope　with　the　disaster.　The　anchor　value　is　modified　before　image　training　by　YOLO　v3,　and　sets　the　corresponding　ROI　for　the　UAV　inspection　line　standard.　In　order　to　quickly　obtain　the　loss　assessment　of　post-disaster　pole　lodging,　this　paper　proposes　a　counting　algorithm　by　using　the　continuous　ordinate　change　of　the　bounding　box　of　the　same　pole　in　front　and　rear　frame　of　video,　so　that　the　classified　counting　of　pole　is　accurate　and　the　detection　precision　is　above　0.9.　The　results　obtained　in　video　test　show　that　this　method　is　effective　in　detecting　and　counting　the　state　of　the　pole　of　overhead　transmission　line　in　distribution　network.　©　2019,　The　Korean　Institute　of　Electrical　Engineers.