基于最近邻链的车牌检测算法

License Plate Detection Algorithm Based on Nearest Neighbor Chains

根据车牌字符的几何特征和空间排列规则,提出了一种基于最近邻链的自适应车牌检测算法,首先,采用自适应阈值分割算法消除光照变化的影响,并采用连通体分析方法消除部分干扰目标;其次,根据车牌字符连通体的区域特征,将宽度和高度都相近的连通体构造为最近邻连通体对,并将最近邻对连接为最近邻链,从而检测出所有可能的车牌区域;最后,利用两组不同长度的方波模板分别对车牌的水平和竖直投影进行匹配,它能够验证候选车牌区域的有效性,并求解所有车牌字符的最佳切分位置实验表明,该算法能够自适应地处理光照不均匀、尺度变化、透视失真、背景干扰以及质量退化等因素的影响,可以有效地检测出复杂背景中的车牌区域.

This paper proposes a nearest neighbor chain based method for adaptive license plate（LP） detection according to the geometrical characteristics and spatial arrangement of license plate characters.Firstly,image is segmented with adaptive binarization method to avoid the problem created by nonuniform illumination,and some undesired image areas are removed by limiting the range of region properties of connected components.Secondly,nearest neighbor pairs are constructed according to region properties of connected components of LP characters,and they are merged into nearest neighbor chains,thus,to detect all the candidate license plate regions.Finally,two variable-length square templates are designed to match horizontal and vertical projections of LP image gradients.It can verify all the candidate LP regions and determine the boundaries of all LP characters simultaneously.Experiments show that this method can deal with nonuniform illumination,LP size change,rotation,background interference,quality degradation and so on,and that it has achieved desired detection result for various types of LP images in complex scenes.