The　detection　of　small　objects　within　intricate　backgrounds　poses　a　significant　challenge　in　the　domain　of　aerial　image　object　detection.　In　this　manuscript,　a　spatial　context-aware　selection　network　(SCASNet)　is　proposed,　which　innovatively　integrates　a　state　space　model　with　the　YOLO　architecture　to　address　this　challenge.　A　spatial　selection　block　and　a　context-aware　block　are　designed　to　form　a　spatial　context-aware　selection　module,　which　can　overcome　the　limitations　of　the　original　state　space　model　in　sequence　modeling,　such　as　insufficient　receptive　fields　and　weak　local　dependency　modeling.　Then,　a　channel　prior　multidimensional　attention　enhancement　module　is　proposed　to　focus　on　key　information　and　optimize　the　extraction　of　spatial　relationships.　It　leverages　multiscale　strip　convolutions　to　map　spatial　relationships　and　dynamically　allocates　weights　across　channel　and　spatial　dimensions.　Finally,　a　content-focused　attention　module　is　designed　in　the　detection　heads　to　fuse　fine-grained　features　from　the　lower　layers　of　the　backbone　network　with　semantic　features　from　the　neck　layers,　which　enhances　the　richness　of　feature　representation.　Extensive　experiments　conducted　on　publicly　available　datasets,　VisDrone,　AI-TOD,　and　SSDD,　demonstrate　the　competitive　performance　of　the　proposed　SCASNet　compared　with　existing　aerial　image　object　detection　models.　©　2008-2012　IEEE.