To　address　the　problem　of　insufficient　detection　accuracy　of　aircraft　targets　in　optical　remote　sensing　images　due　to　complex　backgrounds，small　targets，and　similar　appearances　among　aircraft，an　aircraft　target　detection　algorithm　was　proposed　in　this　paper　based　on　the　YOLOv8n　model　that　integrated　the　global　information　and　the　dual-domain　attention　mechanism　in　optical　remote　sensing　images.　Firstly，the　SPPF_Global　module　was　designed　to　provide　a　global　feature　overview　through　the　global　maximum　pooling　layer，which　helped　the　model　better　distinguish　objects　from　the　background　in　complex　environments.　Secondly，a　dual-domain　attention　mechanism　was　proposed　to　improve　the　attention　to　important　areas　such　as　wing　shape　and　other　distinctive　structures　through　the　information　guidance　of　space　domain　and　channel　domain，and　enhanced　the　ability　to　distinguish　the　nuances　of　different　aircraft　models.　Finally，the　parallel　path　downsampling　method　and　the　Powerful-IoU　loss　function　was　introduced，and　the　adaptive　penalty　factor　was　used　to　accelerate　the　convergence　of　the　model，which　improved　the　recognition　ability　of　the　model　for　small　target　aircraft　and　the　regression　efficiency　of　the　prediction　frame.　The　experimental　results　show　that　compared　with　the　original　YOLOv8n，the　accuracy　rate，recall　rate，mAP50　and　MAP50-95　of　the　proposed　model　on　the　open　data　set　MAR20　are　increased　by　3.　3%，2.　6%，3.　2%　and　2.　6%　respectively.　On　the　NWPU　VHR-10　dataset，the　parameters　are　increased　by　5%，5.　1%，2.　5%　and　0.　3%　respectively，while　the　number　of　parameters　and　the　calculation　amount　are　decreased　by　6.　6%　and　3.　7%　respectively，which　proves　the　efficiency　and　superiority　of　the　proposed　model，and　effectively　improves　the　application　value　of　the　aircraft　target　detection　algorithm　in　optical　remote　sensing　images.　©　2024　Chinese　Academy　of　Sciences.　All　rights　reserved.