Vehicle　detection　and　recognition　based　on　Unmanned　Aerial　Vehicle　(UAV)　remote　sensing　images　is　of　great　significance　in　both　civilian　and　military　domains.　There　is　currently　a　growing　demand　for　real-time,　precise,　and　reliable　vehicle　detection　and　recognition　technologies　due　to　complex　and　diverse　application　scenarios.　In　contrast　to　traditional　close-range　imaging,　UAV　remote　sensing　images　present　a　more　complex　background,　and　the　vehicle　targets　exhibit　characteris-tics　such　as　diverse　types,　multi-scale　variations,　and　occasional　dense　distributions.　This　study　aims　to　address　the　challenges　associated　with　vehicle　detection　and　recognition　in　UAV　remote　sensing　images.　We　have　conducted　targeted　optimization　designs　based　on　the　characteristics　of　UAV　remote　sensing　imaging　to　enhance　the　computational　efficiency,　accuracy,　and　reliability　of　object　detection　and　recognition.　Our　approach　involves　the　development　of　an　improved　algorithm　designed　to　enhance　object　detection　and　recognition　in　UAV　remote　sensing　images.　This　algorithm　has　been　optimized　for　brain-inspired　chips,　enabling　acceleration　in　detection　and　recognition　speed　on　UAV　edge-computing　terminals　to　meet　real-time　requirements.　The　experimental　results　conclusively　indicate　that　the　proposed　algorithm　in　this　paper　significantly　improves　the　accuracy　and　efficiency　of　vehicle　target　detection　in　UAV　remote　sensing　images.　©　2023　IEEE.