Toward　the　fine　detection　of　high-voltage　insulators,　this　paper　proposes　a　framework　for　fine　detection　of　high-voltage　insulators　based　on　infrared　thermal　imaging　data.　The　framework　realizes　high-precision　detection　of　insulators　and　fine　detection　of　the　number　of　insulator　tiles　through　the　improved　YOLOv5s　detection　algorithm　combined　with　the　designed　density　clustering　peak　detector.　The　improved　YOLOv5s　detection　algorithm　embedded　with　the　designed　CDA　attention　mechanism　enhances　the　global　sensing　capability.　Meanwhile,　the　density　clustering　peak　detector　designed　based　on　density　clustering　combined　with　moving　smooth　filtering　realizes　the　fine　counting　of　insulator　tiles　in　infrared　thermal　imaging.　In　order　to　verify　the　effectiveness　of　the　proposed　framework,　an　infrared　thermal　imaging　insulator　detection　dataset　from　the　UAV　perspective　is　built　and　compared　with　typical　detection　models　and　benchmark　models.　The　experimental　results　show　that　the　proposed　method　achieves　to　in　detection　accuracy　to　realizes　the　dual　tasks　of　accurate　insulator　detection　and　fine　counting　of　insulator　tile　number,　which　provides　a　new　solution　for　the　fine　detection　of　insulators　in　high　altitude　power　transmission　lines.　©　2024　IEEE.