Object　detection　in　remote　sensing　images　has　become　a　crucial　component　of　computer　vision.　It　has　been　employed　in　multiple　domains,　including　military　surveillance,　maritime　rescue,　and　military　operations.　However,　the　high　density　of　small　objects　in　remote　sensing　images　makes　it　challenging　for　existing　networks　to　accurately　distinguish　objects　from　shallow　image　features.　These　factors　contribute　to　many　object　detection　networks　that　produce　missed　detections　and　false　alarms,　particularly　for　densely　arranged　objects　and　small　objects.　To　address　the　above　problems,　this　paper　proposes　a　feature　enhancement　feedforward　network　(FEFN),　based　on　a　lightweight　channel　feedforward　module　(LCFM)　and　a　feature　enhancement　module　(FEM).　First,　the　FEFN　captures　shallow　spatial　information　in　images　through　a　lightweight　channel　feedforward　module　that　can　extract　the　edge　information　of　small　objects　such　as　ships.　Next,　it　enhances　the　feature　interaction　and　representation　by　utilizing　a　feature　enhancement　module　that　can　achieve　more　accurate　detection　results　for　densely　arranged　objects　and　small　objects.　Finally,　comparative　experiments　on　two　publicly　challenging　remote　sensing　datasets　demonstrate　the　effectiveness　of　the　proposed　method.