License　plate　recognition　technology　plays　an　important　role　in　traffic　management.　It　is　widely　used　in　parking,　high-speed　and　road　traffic　management.　The　existing　license　plate　recognition　system　is　easy　to　be　disturbed　by　the　external　environment,　and　the　detection　performance　is　poor　in　the　night　scene.　This　paper　investigates　the　problem　of　license　plate　recognition　in　night　vision　scenarios　and　license　plate　tilt.　This　paper　proposes　a　license　plate　recognition　method　with　night　vision　enhancement,　which　can　detect　and　recognize　license　plates　under　extremely　poor　lighting　conditions.　The　method　first　uses　a　night　vision　enhancement　module,　Recursive　Encoder-Decoder　Network　(RED-Net),　and　a　set　of　non-reference　loss　functions　designed　for　properties　of　the　image.　And　then　the　License　Plate　Location　and　Recognition　(LPLR)　system　is　used　to　get　the　license　plate　number.　The　algorithm　is　tested　on　75k　images　of　the　simulated　CCPD　night　dataset.　The　testing　result　that　the　accuracy　of　the　license　plate　recognition　algorithm　after　night　vision　enhancement　can　reach　72.29%　increase　65.5%　than　without　night　vision　enhancement.　©　2022　IEEE.