基于视觉显著图的车牌定位算法

A Novel License Plate Localization Algorithm Based on Saliency Map

本文基于视觉显著图提出了一种新的车牌定位算法。首先利用自底向上的视觉注意模型对输入的彩色图片分别提取出强度、车牌颜色、车牌边框方向三类特征图;然后将特征图整合成视觉显著图,并对视觉显著图进行二值化,得到车牌的候选区域;最后结合输入图片水平方向上的边缘信息,以及车牌的长宽比例、跳变次数等特征,准确提取车牌区域。实验证明,此算法能大量地减少背景环境对定位所带来的伪车牌个数,有很强的环境适应性和抗噪能力,具有较高的定位率。

A novel license plate localization algorithm is presented based on the saliency map. The feature maps with respect to intensity, color and orientation are firstly extracted from the input color RGB image by using the bottom-up visual attention model. Moreover, these feature maps are combined into a saliency map. After the binarization of saliency map, several plate candidates are obtained. Then, vertical edges, the aspect ratio, and hopping frequency of the brightness of the license plate region are used to eliminate the undesired candidates and determine the real license plate region. The comparison experimentations show that the proposed method can reduce the pseudo-plate number due to the background environment and have strong environmental adaptability, anti-noise ability as well as high localization rate.