Objects　in　aerial　images　often　have　arbitrary　orientations　and　variable　shapes　and　sizes.　As　a　result,　accurate　and　robust　object　detection　in　aerial　images　is　a　challenging　problem.　In　this　paper,　an　arbitrary-oriented　object　detection　method　for　aerial　images,　based　on　Dynamic　Deformable　Convolution　(DDC)　and　Self-normalizing　Channel　Attention　Mechanism　(SCAM),　is　proposed;　this　method　uses　ReResNet-50　as　the　backbone　network　to　extract　rotation-equivariant　features.　First,　DDC　is　proposed　as　a　replacement　for　the　conventional　convolution　operation　in　the　Convolutional　Neural　Network　(CNN)　in　order　to　cope　with　various　shapes,　sizes　and　arbitrary　orientations　of　the　objects.　Second,　SCAM　embedded　into　the　high　layer　of　ReResNet-50,　which　allows　the　network　to　enhance　the　important　feature　channels　and　suppress　the　irrelevant　ones.　Finally,　Rotation　Regions　of　Interest　(RRoI)　are　generated　based　on　a　Region　Proposal　Network　(RPN)　and　a　RoI　Transformer　(RT),　and　the　RoI-wise　classification　and　bounding　box　regression　are　realized　by　Rotation-invariant　RoI　Align　(RiRoI　Align).　The　proposed　method　is　comprehensively　evaluated　on　three　publicly　available　benchmark　datasets.　The　mean　Average　Precision　(mAP)　can　reach　80.91%,　92.73%　and　94.1%　on　DOTA-v1.0,　DOTA-v1.5　and　HRSC2016　datasets,　respectively.　The　experimental　results　show　that,　when　compared　with　the　state-of-the-arts　methods,　the　proposed　method　can　achieve　superior　detection　accuracy.　©　2023　by　the　authors.