Recently,　oriented　object　detection　in　remote　sensing　images　has　garnered　significant　attention　due　to　its　broad　range　of　applications.　Early-oriented　object　detection　adhered　to　the　established　general　object　detection　frameworks,　utilizing　the　label　assignment　strategy　based　on　the　horizontal　bounding　box　(HBB)　annotations　or　rotation-agnostic　cost　function.　Such　a　strategy　may　not　reflect　the　large　aspect　ratio　and　rotation　of　arbitrary-oriented　objects　in　remote　sensing　images　and　require　high　parameter-tuning　efforts　in　the　training　process,　which　will　eventually　harm　the　detector　performance.　Furthermore,　the　localization　quality　of　oriented　objects　depends　on　precise　rotation　angle　prediction,　exacerbating　the　inconsistency　between　classification　and　regression　tasks　in　oriented　object　detection.　To　address　these　issues,　we　propose　the　Gaussian　distribution　cost　optimal　transport　assignment　(GCOTA)　and　decoupled　layer　attention　angle　head　(DLAAH).　Specifically,　GCOTA　utilizes　a　Gaussian　distribution-based　cost　function　for　the　optimal　transport　(OT)　label　assignment　in　the　training　process,　alleviating　the　impact　of　rotation　angle　and　large　aspect　ratio　in　remote　sensing　images.　DLAAH　predicts　rotation　angle　independently　and　incorporates　layer　attention　to　obtain　the　task-specific　features　based　on　the　shared　FPN　features,　enhancing　the　angle　prediction　and　improving　consistency　across　different　tasks.　Based　on　these　proposed　components,　we　present　an　anchor-free　oriented　detector,　namely,　Gaussian　distribution　and　task-decoupled　head　oriented　detector　(GTDet)　and　a　multiclass　ship　detection　dataset　in　real　scenarios　(CGWX),　which　provides　a　benchmark　for　fine-grained　object　recognition　in　remote　sensing　images.　Comprehensive　experiments　are　conducted　on　CGWX　and　several　public　challenging　datasets,　including　DOTAv1.0,　and　HRSC2016,　to　demonstrate　that　our　method　achieves　superior　performance　on　oriented　object　detection　tasks.　The　code　is　available　at　https://github.com/WUTCM-Lab/GTDet.