Ship　detection　is　a　significant　and　challenging　task　in　remote　sensing.　Due　to　the　arbitrary-oriented　property　and　large　aspect　ratio　of　ships,　most　of　the　existing　detectors　adopt　rotation　boxes　to　represent　ships.　However,　manual-designed　rotation　anchors　are　needed　in　these　detectors,　which　causes　multiplied　computational　cost　and　inaccurate　box　regression.　To　address　the　abovementioned　problems,　an　anchor-free　rotation　ship　detector,　named　GRS-Det,　is　proposed,　which　mainly　consists　of　a　feature　extraction　network　with　selective　concatenation　module　(SCM),　a　rotation　Gaussian-Mask　model,　and　a　fully　convolutional　network-based　detection　module.　First,　a　U-shape　network　with　SCM　is　used　to　extract　multiscale　feature　maps.　With　the　help　of　SCM,　the　channel　unbalance　problem　between　different-level　features　in　feature　fusion　is　solved.　Then,　a　rotation　Gaussian-Mask　is　designed　to　model　the　ship　based　on　its　geometry　characteristics,　which　aims　at　solving　the　mislabeling　problem　of　rotation　bounding　boxes.　Meanwhile,　the　Gaussian-Mask　leverages　context　information　to　strengthen　the　perception　of　ships.　Finally,　multiscale　feature　maps　are　fed　to　the　detection　module　for　classification　and　regression　of　each　pixel.　Our　proposed　method,　evaluated　on　ship　detection　benchmarks,　including　HRSC2016　and　DOTA　Ship　data　sets,　achieves　state-of-the-art　results.