Synthetic　aperture　radar　(SAR)　is　an　active　imaging　microwave　sensor.　Since　SAR　imaging　is　robust　against　weather,　light,　and　other　external　conditions,　Ship　Detection　based　on　SAR　Imaging　has　high　research　and　application　values.　However,　the　variety　in　size,　direction,　background　and　arrangement　poses　great　challenges　to　SAR　image　ship　detection.　Due　to　the　lack　of　effective　constrains,　plenty　of　boxes　predicted　by　the　existing　methods　are　quite　different　from　groundtruth,　resulting　in　IOU　between　predicted　boxes　and　groundtruth　being　smaller　than　the　threshold　and　generates　false　alarms.　Therefore,　in　this　letter　we　propose　a　method　of　noisy　ship　direction　classification,　which　uses　the　ship　pixel　distribution　in　SAR　images　to　classify　ship　objects　into　different　directions,　so　as　to　reduce　the　shape　difference　between　boxes　and　groundtruth.　Experimental　results　show　that　by　combining　with　YOLOv5　framework,　our　method　can　significantly　improve　the　ship＇s　detection　performance,　and　reach　state-of-the-art.　©　2021　IEEE.