Supervision　of　wearing　insulation　gloves　during　electrical　equipment　inspection　is　the　top　priority　of　safety　protection　in　indoor　and　outdoor　high-voltage　power　areas.　However,　in　the　actual　scene,　there　are　usually　incomplete　features　of　monitoring　targets　and　insufficient　feature　information　of　small-scale　targets　caused　by　factors　such　as　occlusion　and　distance,　which　leads　to　low　accuracy　of　inspection　personnel　wearing　insulation　gloves.　In　view　of　the　above　situation,　this　paper　proposes　an　improved　testing　model　for　insulation　gloves.　Firstly,　SCAM　attention　mechanism　and　M-MHSA　module　were　integrated　into　the　feature　extraction　network　to　improve　the　ability　of　the　model　to　extract　global　information　and　target　channel　features　combined　with　multi-head　attention　mechanism.　Adding　small　target　detection　layer　and　using　weighted　bidirectional　feature　pyramid　network　(BiFPN)　to　carry　out　multi-scale　feature　fusion　can　improve　the　detection　ability　of　the　model　on　different　scale　targets.　The　experimental　results　show　that　the　improved　algorithm　achieves　93.27%　average　accuracy　and　32frame/s　detection　speed　in　indoor　and　outdoor　high-voltage　electricity　usage　scenarios,　which　has　good　performance　in　the　detection　task　of　insulation　gloves.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2024.