In　object　detection　of　remote　sensing　images,　anchor-free　detectors　often　suffer　from　false　boxes　and　sample　imbalance,　due　to　the　use　of　single　oriented　features　and　the　key　point-based　boxing　strategy.　This　paper　presents　a　simple　and　effective　anchor-free　approach-RatioNet　with　less　parameters　and　higher　accuracy　for　sensing　images,　which　assigns　all　points　in　ground-truth　boxes　as　positive　samples　to　alleviate　the　problem　of　sample　imbalance.　In　dealing　with　false　boxes　from　single　oriented　features,　global　features　of　objects　is　investigated　to　build　a　novel　regression　to　predict　boxes　by　predicting　width　and　height　of　objects　and　corresponding　ratios　of　l_ratio　and　t_ratio,　which　reflect　the　location　of　objects.　Besides,　we　introduce　ratio-center　to　assign　different　weights　to　pixels,　which　successfully　preserves　high-quality　boxes　and　effectively　facilitates　the　performance.　On　the　MS-COCO　test–dev　set,　the　proposed　RatioNet　achieves　49.7%　AP.　©　2021　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.