Face　occlusion　is　a　common　problem　in　real　life.　Aiming　at　the　problem　that　the　face　detection　effect　is　poor　when　there　is　occlusion　in　the　natural　environment,　this　paper　proposes　the　RetinaNet　occlusion　face　detection　network　based　on　the　fusion　image　enhancement　network　and　the　attention　mechanism.　Traditional　RetinaNet,　as　a　typical　single-stage　detector,　does　not　explicitly　give　the　process　of　extracting　candidate　regions　and　directly　obtains　the　results　of　object　detection.　Compared　with　the　two-stage　detector,　the　processing　speed　is　faster,　but　the　accuracy　will　also　be　reduced.　In　this　paper,　the　attention　mechanism　and　image　enhancement　strategy　are　introduced　to　improve　the　detection　accuracy　while　ensuring　the　detection　speed.　The　experimental　results　on　the　MAFA　dataset　show　that　the　RetinaNet　model　based　on　the　attention　mechanism　proposed　in　this　paper　achieves　89.7%　accuracy　in　detecting　occluded　faces.　The　feasibility　and　effectiveness　of　the　model　are　verified.　©　Published　under　licence　by　IOP　Publishing　Ltd.