The　Unmanned　Aerial　Vehicle　(UAV)　inspection　mode　has　been　gradually　implemented　in　the　power　system.　The　UAV　inspection　image　is　checked　by　the　target　detection　technology,　but　there　is　no　high-precision　target　detection　algorithm　as　the　technical　support.　In　this　regard,　this　paper　proposes　a　target　detection　algorithm　based　on　the　improved　RetinaNet　which　is　suitable　for　transmission　lines　defect　detection.　In　this　algorithm,　the　shortcomings　of　the　RetinaNet　anchor　frame　extraction　mechanism　based　on　Apriori　are　corrected.　At　the　same　time,　the　number　and　size　of　anchor　frames　are　redesigned　by　using　the　improved　K-means　+　+　algorithm,　so　that　the　anchor　frame　of　the　improved　algorithm　gets　the　highest　average　IoU　(Intersection　over　Union)　value,　which　matches　the　actual　size　of　the　transmission　line　defects.　Then,　in　RetinaNet,　the　feature　pyramid　network　based　on　DenseNet　is　built　as　the　backbone　network　to　improve　the　model　accuracy　and　make　the　model　lighter.　The　improved　model　is　trained　and　tested　by　using　the　data　set　of　transmission　line　defects　for　validation.　The　results　show　that　the　proposed　method　has　advantages　and　effectiveness　in　the　detection　of　transmission　line　defects,　and　meets　the　requirements　of　intelligent　inspection　in　terms　of　accuracy.　(C)　2020　The　Authors.　Published　by　Elsevier　Ltd.