The　detection　of　insulators　with　cluttered　backgrounds　in　aerial　images　is　a　challenging　task　for　an　automatic　transmission　line　inspection　system.　In　this　paper,　we　propose　an　effective　and　reliable　insulator　detection　method　based　on　a　deep　learning　technique　for　aerial　images.　In　the　proposed　deep　detection　approach,　the　single　shot　multibox　detector　(SSD),　a　powerful　deep　meta-architecture,　is　incorporated　with　a　strategy　of　two-stage　fine-tuning.　The　SSD-based　model　can　realize　automatic　multi-level　feature　extractor　from　aerial　images　instead　of　manually　extracting　features.　Inspired　by　transfer　learning,　a　two-stage　fine-tuning　strategy　is　implemented　using　separate　training　sets.　In　the　first　stage,　the　basic　insulator　model　is　obtained　by　fine-tuning　the　COCO　model　with　aerial　images,　including　different　types　of　insulators　and　various　backgrounds.　In　the　second　stage,　the　basic　model　is　fine-tuned　by　the　training　sets　of　the　specific　insulator　types　and　specific　situations　to　be　detected.　After　the　two-stage　fine-tuning,　the　well-trained　SSD　model　can　directly　and　accurately　identify　the　insulator　by　feeding　the　aerial　images.　The　results　show　that　both　the　porcelain　insulator　and　composite　insulator　can　be　quickly　and　accurately　identified　in　the　aerial　images　with　complex　background.　The　proposed　approach　can　enhance　the　accuracy,　efficiency,　and　robustness　significantly.