Object　detection　is　one　of　the　most　concerned　issues　in　computer　vision,　especially　in　the　real　scene,　its　accuracy,　model　size,　inference　speed,　IoU　and　other　indicators　have　strict　requirements,　so　the　manual　construction　of　neural　network　model　often　can　not　get　good　results.　This　work　uses　neural　architecture　search　(NAS)　as　a　tool　to　search　the　lightweight　object　detection　network　that　meets　the　requirements　of　real　scenes　by　combining　manual　and　automatic　methods.　In　practice,　we　take　the　intelligent　driving　as　the　background,　using　classification　data　sets　with　proxy,　shared　weight　and　migration-based　algorithm　to　search　object　detection　network.　The　first　step　to　search　out　the　sensitive　to　intelligent　driving　of　backbone,　the　second　step　migrates　the　backbone　to　the　object　detection　network,　then　using　the　actual　data　collected　by　the　network　further　fine-tuning.　Finally,　a　neural　network　model　satisfying　the　requirements　is　obtained　According　to　the　experimental　results,　we　can　see　that　our　work　in　the　model　size,　precision,　inference　speed　and　so　on　comprehensive　index　made　a　balanced　and　excellent　results,　proves　the　effectiveness　of　this　method,　also　represents　this　work　for　the　practical　application　of　neural　network　architecture　search　provides　effective　solution.　©　2022　IEEE.