Windows　are　key　components　of　building　facades.　Detecting　windows,　crucial　to　3D　semantic　reconstruction　and　scene　parsing,　is　a　challenging　task　in　computer　vision.　Early　methods　try　to　solve　window　detection　by　using　hand-crafted　features　and　traditional　classifiers.　However,　these　methods　are　unable　to　handle　the　diversity　of　window　instances　in　real　scenes　and　suffer　from　heavy　computational　costs.　Recently,　convolutional　neural　networks　based　object　detection　algorithms　attract　much　attention　due　to　their　good　performances.　Unfortunately,　directly　training　them　for　challenging　window　detection　cannot　achieve　satisfying　results.　In　this　paper,　we　propose　an　approach　for　window　detection.　It　involves　an　improved　Faster　R-CNN　architecture　for　window　detection,　featuring　in　a　window　region　proposal　network,　an　RoI　feature　fusion　and　a　context　enhancement　module.　Besides,　a　post　optimization　process　is　designed　by　the　regular　distribution　of　windows　to　refine　detection　results　obtained　by　the　improved　deep　architecture.　Furthermore,　we　present　a　newly　collected　dataset　which　is　the　largest　one　for　window　detection　in　real　street　scenes　to　date.　Experimental　results　on　both　existing　datasets　and　the　new　dataset　show　that　the　proposed　method　has　outstanding　performance.