Target　recognition　and　temperature　extraction　of　the　typical　component　of　gas　insulated　switchgear　(GIS)　are　the　key　to　realizing　the　infrared　intelligent　detection　of　equipment　heating　state.　In　this　paper,　an　improved　YOLOv4　algorithm　based　on　convolutional　block　attention　module　(CBAM)　is　proposed　to　achieve　rapid　target　detection　and　hot　spot　temperature　extraction　of　GIS　bus,　disconnector　and　other　components.　Firstly,　the　original　infrared　images　are　acquired　at　a　substation　site,　and　an　infrared　dataset　containing　typical　GIS　components　is　constructed　by　sharpening　the　images　and　marking　components.　Then,　the　deep　separable　convolutional　network　is　used　to　reduce　the　amount　of　model　parameters,　and　the　CBAM　is　integrated　to　optimize　the　recognition　ability　of　the　model,　on　the　basis　of　which　a　GIS　infrared　component　target　rapid　detection　algorithm　with　improved　YOLOv4　is　constructed.　Finally,　the　gray-scale　difference　method　is　used　to　extract　the　temperature　value　of　the　hot　area　for　the　detected　typical　target　components　of　GIS.　The　results　show　that　the　proposed　algorithm　can　achieve　a　recognition　speed　of　31.5　frame　per　second　and　an　recognition　accuracy　of　82.3%　on　the　GIS　infrared　feature　dataset,　which　is　significantly　better　than　other　target　algorithms.　The　error　between　the　calculated　value　and　the　measured　value　of　temperature　rise　of　GIS　components　is　within　±1℃.　The　algorithm　proposed　in　this　paper　can　be　deployed　in　edge　intelligent　terminals　such　as　unmanned　aerial　vehicles　and　inspection　trolleys　to　achieve　refined　identification　and　rapid　diagnosis　of　the　temperature　rise　status　of　on-site　GIS　equipment,　thus　improving　the　digitalization　and　intelligence　level　of　health　management　of　GIS.　©　2023,　Editorial　Department　of　Electric　Power　Engineering　Technology.　All　rights　reserved.