The　slippage　fault　diagnosis　of　dampers　in　aerial　images　is　a　challenging　task　for　an　automatic　transmission　line　inspection　system.　In　this　paper,　an　effective　and　reliable　slippage　fault　diagnosis　method　based　on　a　deep　learning　technique　and　distance　constraint　method　for　aerial　images　is　proposed.　In　the　detection　stage,　the　Faster　Regions　with　Convolutional　Neural　Network　(R-CNN)　is　employed.　It　takes　the　aerial　images　as　input,　detects　the　dampers　in　the　image　by　learning　the　basic　features　of　dampers,　and　outputs　the　coordinates　of　the　dampers　in　a　single　image.　In　the　diagnosis　stage,　the　distance　between　adjacent　dampers　in　the　horizontal　axis　is　calculated　through　the　damper　coordinates　obtained　in　the　detection　stage,　and　the　relationship　between　the　distance　and　the　selected　threshold　is　the　judgment　basis　of　slippage　fault　diagnosis.　The　results　show　that　the　slippage　fault　of　dampers　can　be　quickly　and　accurately　diagnosed　in　the　aerial　images　by　the　proposed　method.