The　variation　in　window　opening　areas　by　individuals　can　lead　to　significant　differences　in　air　change　rates,　indirectly　affecting　indoor　air　quality　and　building　energy　consumption.　While　existing　research　primarily　focuses　on　whether　windows　are　open　or　closed　due　to　data　limitations,　this　study　proposes　a　novel　detection　method　for　window　opening　areas　based　on　the　one-stage　object　detector　You　Only　Look　Once　version　5　(YOLOv5).　Two　sub-models,　YOLOv5s　and　YOLOv5l,　were　evaluated　using　training　datasets　of　various　sizes,　and　their　performance　was　validated　with　test　datasets　and　a　video.　The　YOLOv5l_927　model　emerged　as　the　most　generalizable,　achieving　a　recognition　accuracy　exceeding　91%　in　both　the　training　and　test　sets,　outperforming　the　Faster　R-CNN　model　by　12%-15%.　Moreover,　the　YOLOv5l_927　model　demonstrated　real-time　detection　capabilities　in　a　video,　with　an　inference　speed　of　89　frames　per　second.　The　application　of　this　method　to　monitor　nine　bedrooms　revealed　that　50%　of　the　window　opening　operations　involved　adjustments　between　different　opening　areas.　Occupants　were　more　inclined　to　open　the　window　area　to　75-100%.　This　innovative　detection　method　enhances　the　accuracy　of　building　energy　simulations　by　refining　window　opening　behavior　based　on　opening　area.