Recently,　with　the　development　of　deep　learning,　Automatic　License　Plate　Recognition　(ALPR)　has　made　great　progress,　However,　there　are　still　many　challenges　to　accomplish　license　plate　(LP)　recognition　under　various　traffic　scenarios.　One　of　them　is　the　detection　speed　and　recognition　speed,　and　the　other　is　the　difficulty　to　recognize　the　low　resolution　and　highly　tilted　LP　images.　In　this　paper,　we　present　a　two-stage　ALPR　framework　to　achieve　efficient　LP　detection　and　recognition　in　unconstrained　scenarios.　Our　LP　detector　is　based　on　improved　Yolov3-tiny,　and　we　also　propose　a　lightweight　recognition　network　MRNet　based　on　multi-scale　features.　In　order　to　improve　the　inference　speed,　we　abandoned　the　rectification　of　LP　images,　and　RNNs　that　are　difficult　to　compute　in　parallel.　Additionally,　we　also　propose　a　license　plate　data　augmentation　method,　which　achieves　more　effective　augmentation　and　improves　the　generalization　ability　of　the　network　through　secondary　random　and　hyperparametric　search.　As　an　additional　contribution,　we　provide　a　challenging　dataset　collected　from　real-world　driving　recorders.　The　dataset　is　for　multiple　LPs　in　a　single　image,　which　makes　up　for　the　lack　of　public　datasets　in　multiple　LP　scenarios.　We　evaluated　the　results　on　five　datasets　and　showed　that　we　achieved　the　best　performance　on　almost　all　test　sets,　achieving　99.8%　accuracy　on　CCPD　with　more　than　180,000　license　plate　test　sets.　In　terms　of　speed,　the　inference　speed　for　detecting　license　plates　reaches　751　FPS,　and　the　fastest　inference　time　for　recognizing　a　single　license　plate　takes　only　2.9　ms.