基于HSV和MB_LBP特征的级联Adaboost车牌检测算法
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TP391.4

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国家自然科学基金


Detection Algorithm of Cascaded Adaboost License Plate Based on HSV color model and MB_LBP Features
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    摘要:

    车牌检测作为车牌识别系统中的重要环节,直接影响着车牌识别的准确度。为提高车牌的检测率和检测速度,提出了一种基于HSV颜色模型和多分块局部二值模式(MB_LBP)特征的级联Adaboost车牌检测方法。首先将车牌图像由RGB颜色空间转换到HSV颜色空间,统计蓝色像素占车牌总像素的比例,来构建第一层强分类器;其次对车牌字符样本提取MB_LBP特征,利用Adaboost分类器训练方法进行特征选择及分类器训练,最后利用Cascade结构检测法形成一种新的车牌检测算法。实验表明,本文算法有效的提高了车牌检测率和检测速度。

    Abstract:

    License plate detection is an important part of the license plate recognition system, which deeply affects the accuracy of license plate recognition. A method for cascade adaboost license plate detection based on HSV color model and multi-block local binary patterns (MB_LBP) is presented to realize fast and accurate license plate detection and recognition. Firstly, the license plate image is transformed from RGB color space to HSV color space, and the ratio of the blue pixels to the total pixels of the license plate is counted to construct the first class strong classifier. Then, the MB_LBP feature is extracted from the license plate character samples, and the feature selection and the classifier training are carried out by using the Adaboost classifier training method. Finally, a new license plate detection algorithm is formed by using the Cascade structure detection method. Experiments results show that the license plate detector improves the detection rate and the detection speed.

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引用本文格式: 马永杰,李欢,刘姣姣. 基于HSV和MB_LBP特征的级联Adaboost车牌检测算法[J]. 四川大学学报: 自然科学版, 2018, 55: 290.

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  • 收稿日期:2017-01-06
  • 最后修改日期:2017-04-06
  • 录用日期:2017-05-23
  • 在线发布日期: 2018-03-13
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