Object　detection　has　made　tremendous　progress　in　natural　images　over　the　last　decade.　However,　the　results　are　hardly　satisfactory　when　the　natural　image　object　detection　algorithm　is　directly　applied　to　satellite　images.　This　is　due　to　the　intrinsic　differences　in　the　scale　and　orientation　of　objects　generated　by　the　bird＇s-eye　perspective　of　satellite　photographs.　Moreover,　the　background　of　satellite　images　is　complex　and　the　object　area　is　small;　as　a　result,　small　objects　tend　to　be　missing　due　to　the　challenge　of　feature　extraction.　Dense　objects　overlap　and　occlusion　also　affects　the　detection　performance.　Although　the　self-attention　mechanism　was　introduced　to　detect　small　objects,　the　computational　complexity　increased　with　the　image＇s　resolution.　We　modified　the　general　one-stage　detector　YOLOv5　to　adapt　the　satellite　images　to　resolve　the　above　problems.　First,　new　feature　fusion　layers　and　a　prediction　head　are　added　from　the　shallow　layer　for　small　object　detection　for　the　first　time　because　it　can　maximally　preserve　the　feature　information.　Second,　the　original　convolutional　prediction　heads　are　replaced　with　Swin　Transformer　Prediction　Heads　(SPHs)　for　the　first　time.　SPH　represents　an　advanced　self-attention　mechanism　whose　shifted　window　design　can　reduce　the　computational　complexity　to　linearity.　Finally,　Normalization-based　Attention　Modules　(NAMs)　are　integrated　into　YOLOv5　to　improve　attention　performance　in　a　normalized　way.　The　improved　YOLOv5　is　termed　SPH-YOLOv5.　It　is　evaluated　on　the　NWPU-VHR10　dataset　and　DOTA　dataset,　which　are　widely　used　for　satellite　image　object　detection　evaluations.　Compared　with　the　basal　YOLOv5,　SPH-YOLOv5　improves　the　mean　Average　Precision　(mAP)　by　0.071　on　the　DOTA　dataset.