Object　detection　algorithms　for　satellite　remote　sensing　images　have　made　some　progress　over　the　years,　and　this　task　has　also　had　an　important　impact　in　the　military　and　civilian　fields.　The　existing　research　on　remote　sensing　image　target　detection　tasks　mainly　focuses　on　the　optimization　and　improvement　of　the　algorithm　on　GPU.　In　practical　application　scenarios,　edge　devices　such　as　satellites　and　drones　cannot　perform　target　detection　and　recognition　tasks　on　the　collected　remote　sensing　image　data　in　real-time.　The　remote　sensing　image　detection　and　recognition　algorithm　for　edge-end　deployment　can　better　meet　the　timeliness　requirements　of　remote　sensing　image　detection　and　recognition　tasks.　Therefore,　we　propose　an　intelligent　detection　and　recognition　algorithm　for　satellite　remote-sensing　images　deployed　on　a　brain-like　chip.　Firstly,　we　optimize　and　improve　the　existing　advanced　object　detection　algorithm　for　the　edge-end　deployment　task.　We　introduce　the　latest　lightweight　convolution　GSConv　into　the　detection　network　and　refer　to　the　design　method　of　slim-neck　to　reconfigure　the　head　of　the　detection　network.　Then　we　introduce　a　lightweight　triple　attention　mechanism　into　the　backbone　of　the　detection　network　to　improve　the　detection　accuracy,　which　further　improves　the　detection　accuracy　while　maintaining　lightweight.　After　that,　we　deploy　the　algorithm　on　Lynxi　KA200　brain-like　chip　for　further　inference　testing.　We　also　compare　and　analyze　several　mainstream　object　detection　algorithms　and　their　variants,　and　deploy　them　on　the　brain-like　chip.　We　conduct　experiments　on　three　datasets,　and　the　results　show　that　the　algorithm　we　use　outperforms　other　methods　in　both　detection　accuracy　and　inference　speed　on　the　brain-like　chip.　©　2023　IEEE.