Conventional　computer　vision　systems　detect　object　after　super-resolution　(SR)　or　image　reconstruction　of　the　whole　image,　which　is　not　an　economical　manner.　By　imitating　the　visual　system　of　human　beings,　we　proposed　the　bionic　vision　system　(BVS),　which　is　mainly　composed　by　three　parts:　object　detection　by　visual　attention　model,　object-oriented　SR　reconstruction　and　object　recognition　by　convolutional　neural　networks.　The　visual　attention　model　contains　both　bottom-up　and　top-down　cues.　The　bottom-up　cues　integrate　low-level　features　by　the　feature　integration　theory.　An　Adaboost　detector　imitates　the　top-down　cues.　Sparse　coding　and　compressed　sensing　reconstruction　realize　the　object-oriented　SR　reconstruction.　The　BVS　was　validated　on　license　plate　recognition　task.　Both　detection　performance　and　SR　reconstruction　performance　are　tested.　Besides　of　these,　we　also　test　the　final　recognition　rate,　all　the　experimental　results　are　quite　encouraging.