Due　to　the　low　automation　level　and　low　working　efficiency　of　the　existing　drone　spraying　technology,　it　is　difficult　to　meet　the　needs　of　large-scale　applications　and　diversified　requirements.　Therefore,　this　paper　designs　a　CGSP　detection　model　for　insulator　detection　and　an　online　optimization　Kalman　tracking　algorithm　based　on　the　BP　neural　network,　effectively　improving　detection　efficiency　and　accuracy.　An　online　coating　system　is　established.　Real-time　analysis　and　adjustment　of　coating　coverage　and　spraying　uniformity　are　achieved　through　image　differential　analysis,　color　histogram,　and　Haralick　texture　feature　algorithm,　ensuring　that　the　sprayed　blocks　meet　the　standard　values.　The　experimental　results　show　that　the　system　implemented　in　this　study　realizes　a　semi-automatic　closed-loop　process　of　insulator　target　recognition,　flight　tracking,　and　online　spraying　assisted　by　artificial　intelligence　(AI).　©　2025　Institute　of　Physics　Publishing.　All　rights　reserved.