Object　detectors　usually　required　manual　design　of　sliding　windows,　but　they　usually　faced　the　shortage　of　feature　generalization　capabilities.　Even　the　development　of　modern　mainstream　anchor　base　detectors　also　required　artificial　design　scale　and　ratio　of　the　anchor.　This　is　a　complicated　and　tedious　process,　and　the　quality　of　final　effect　needs　to　be　determined　through　multiple　experiments.　In　this　paper,　we　present　a　new　structure　of　object　detection　that　choosing　anchor　free　detection　method,　treat　the　object　as　entirety　with　advanced　semantic　features,　the　convolution　predictor　scans　all　regions　and　activated　by　these　advanced　features.　Compared　to　anchor　base　method,　the　detector＇s　activated　object　is　the　high-level　feature　of　the　target,　and　therefore　it　is　not　vulnerable　to　complex　and　changeable　background　environments.　In　addition,　we　set　the　center　of　gravity　of　instance　as　the　key　point,　which　effectively　avoids　the　shift　of　subject　area　of　interest　caused　by　the　special　shape　pull;　using　the　center　of　gravity　combined　with　the　height　and　width　information　provided　by　the　anchor　box,　resulting　asymmetric　Gaussian　mask　can　play　better　effect　in　crowded　scenarios;　add　FPN　structure　to　the　detection　network　aim　to　enhanced　the　generalization　ability　of　the　detector　for　objects　of　different　scales.　By　designing　a　simple　structured　object　detection　network　MFGP,　we　achieved　considerable　performance　comparable　to　complex　structured　object　detector,　which　has　a　good　performance　on　the　object　detection　dataset　COCO　and　the　pedestrian　detection　dataset　CityPersons.　The　best　neutralization　of　accuracy　and　speed　was　achieved　on　the　COCO　dataset,　reaching　41.2%　AP　when　18　FPS;　on　the　reasonable　subset　of　the　CityPersons　dataset,　it　reached　10.9%　MR-2　without　significantly　increasing　the　test　time.　©　2020　IEEE.