期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 19, 期 2, 页码 507-517出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2017.2784093
关键词
License plate detection; convolutional neural network; MD-YOLO; intersection over union; multi-direction
资金
- GD-NSF [2017A030312006]
- National Key Research and Development Plan of China [2016YFB1001405]
- GZSTP [201607010227]
- GDSTP [2015B010131004, 2015B010101004]
This paper presents a novel convolutional neural network (CNN)-based method for high-accuracy real-time car license plate detection. Many contemporary methods for car license plate detection are reasonably effective under the specific conditions or strong assumptions only. However, they exhibit poor performance when the assessed car license plate images have a degree of rotation, as a result of manual capture by traffic police or deviation of the camera. Therefore, we propose the a CNN-based MD-YOLO framework for multi-directional car license plate detection. Using accurate rotation angle prediction and a fast intersection-over-union evaluation strategy, our proposed method can elegantly manage rotational problems in real-time scenarios. A series of experiments have been carried out to establish that the proposed method outperforms over other existing state-of-the-art methods in terms of better accuracy and lower computational cost.
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