Aiming　at　the　problem　that　the　proportion　of　insulators　in　power　equipment　images　is　small　and　they　are　easy　to　miss　detection,　this　paper　proposes　an　insulator　detection　method　based　on　cross-connected　convolutional　neural　network　for　the　power　equipment　image.　Firstly,　the　convolutional　layers　of　the　last　three　layers　of　the　network　are　connected　with　the　fully-connected　layer　in　the　regional　proposal　network　(RPN),　and　the　three-layer　convolution　features　are　simultaneously　sent　to　the　classification　layer　and　the　regression　layer　to　obtain　a　series　of　high　quality　insulator　candidate　regions.　Secondly,　the　region　proposals　are　input　into　the　insulator　detection　sub-network,　and　the　region　of　interest　(ROI)　features　of　candidate　regions　are　sent　to　the　cascaded　Adaboost　classifier　to　detect　insulators.　Evaluations　are　performed　and　comparative　experiments　are　conducted　based　on　the　candidate　region　generation　methods.　The　results　show　that　the　candidate　regions　obtained　by　the　proposed　method　have　high　recall　rates　and　they　focus　more　on　the　positions　of　the　insulators,　and　the　accuracy　of　the　insulator　detection　is　10%　higher　than　the　conventional　methods.　The　proposed　method　can　effectively　recognize　and　locate　insulators　of　different　sizes　with　complex　background.　©2019　Automation　of　Electric　Power　Systems　Press.