Generally,　the　interesting　objects　in　aerial　images　are　completely　different　from　objects　in　nature,　and　the　remote　sensing　objects　in　particular　tend　to　be　more　distinctive　in　aspect　ratio.　The　existing　convolutional　networks　have　equal　aspect　ratios　of　the　receptive　fields,　which　leads　to　receptive　fields　either　containing　non-relevant　information　or　being　unable　to　fully　cover　the　entire　object.　To　this　end,　we　propose　Horizontal　and　Vertical　Convolution,　which　is　a　plug-and-play　module　to　address　different　aspect　ratio　problems.　In　our　method,　we　introduce　horizontal　convolution　and　vertical　convolution　to　expand　the　receptive　fields　in　the　horizontal　and　vertical　directions,　respectively,　to　reduce　redundant　receptive　fields,　so　that　remote　sensing　objects　with　different　aspect　ratios　can　achieve　better　receptive　fields　coverage,　thereby　achieving　more　accurate　feature　representation.　In　addition,　we　design　an　attention　module　to　dynamically　aggregate　these　two　sub-modules　to　achieve　more　accurate　feature　coverage.　Extensive　experimental　results　on　the　DOTA　and　HRSC2016　datasets　show　that　our　HVConv　achieves　accuracy　improvements　in　diverse　detection　architectures　and　obtains　SOTA　accuracy　(mAP　score　of　77.60%　with　DOTA　single-scale　training　and　mAP　score　of　81.07%　with　DOTA　multi-scale　training).　Various　ablation　studies　were　conducted　as　well,　which　is　enough　to　verify　the　effectiveness　of　our　model.