In　order　to　ensure　the　security　of　building　facades　in　key　areas　and　improve　the　security　detection　efficiency　of　security　personnel　on　building　facades,　this　paper　proposes　an　improved　YOLO　v3-based　algorithm　for　open　window　detection　recognition　in　building　facade　images,　which　extracts　open　window　features　from　images　to　make　predictions　on　full　images　by　convolutional　neural　network.　Firstly,　due　to　the　absence　of　publicly　available　window　datasets　in　the　network　and　the　high　number　of　window　types　that　exist　in　reality,　a　self-constructed　window　dataset　containing　13573images　of　open　windows　is　used　to　train　and　test　the　window　detection　model.　The　data　set　is　then　clustered　by　the　K-Means　clustering　algorithm　to　select　an　Anchor　Box　more　suitable　for　window　detection,　which　draws　on　the　ShuffleNet　idea　to　strengthen　the　feature　extraction　method,　and　then　optimizes　the　network　structure　of　YOLO　v3.　Finally,　a　block　detection　mechanism　is　introduced　to　effectively　enhance　the　network＇s　ability　to　detect　small　dense　targets;　experimental　results　show　that　the　method　improves　the　accuracy　and　speed　of　window　detection　and　reduces　the　workload　of　security　personnel　in　key　areas　to　manually　check　for　windows　on　both　sides　of　the　street.　Fire　detection,　missing　and　falling　bricks　on　the　floor,　and　overhead　throw　detectionare　of　great　importance.　©　2021　Published　under　licence　by　IOP　Publishing　Ltd.