In　order　to　accurately　identify　small　target　insulators　and　defects　in　UAV　inspection　graphics,　a　defect　detection　method　for　transmission　line　insulators　based　on　improved　deep　learning　target　detection　network　namely　YOLOv4　was　proposed.　First,　sufficient　insulator　images　were　collected　by　the　means　of　drone　and　data　enhancement　to　construct　insulator　dataset.　Secondly,　insulator　image　data　set　was　used　to　train　YOLOv4　network.　During　the　training　process,　multi-stage　transfer　learning　strategy　and　cosine　annealing　attenuation　learning　rate　method　were　adopted　to　improve　the　training　speed　and　overall　performance　of　the　network.　Finally,　in　the　test　process,　the　super-resolution　reconstruction　generative　adversarial　network　was　introduced　to　generate　high-quality　images　for　low-confidence　images,　and　then　the　test　was　carried　out　again　to　improve　identification　ability　of　small　objects.　The　experimental　results　show　that,　compared　with　Faster　R-CNN　and　YOLOv3,　the　average　classification　accuracy　and　detection　rate　per　frame　of　the　proposed　algorithm　are　greatly　improved　and　the　performance　is　excellent.　©　2021,　Harbin　University　of　Science　and　Technology　Publication.　All　right　reserved.