多特征融合的车牌定位算法

针对使用单一特征在复杂场景下车牌定位效果不佳的问题,提出了一种融合了边缘、颜色、纹理等多种特征的车牌定位算法。该算法将定位过程分为假设生成和假设检验两个阶段:在假设生成阶段,使用特征点检测、形态学作为主要技术手段,利用车牌的字符纹理和颜色特征生成候选车牌;在假设检验阶段,使用灰度投影作为技术手段,利用车牌结构的固有特征验证候选并实现定位。实验结果表明:在包含实际场景的车牌图像库中,定位成功率可以达到96.6%,精确度可以达到95.4%,验证了多特征融合算法的合理性和有效性。

Vehicle license plate localization algorithm based on multi-feature fusion

The single feature based vehicle license plate localization algorithms are hard to be adapted to the complex environment. In order to solve the problem, a multi-feature fusion algorithm was proposed, which made use of multi-features such as edge, color and texture. The localization process was divided into two phases: Hypothesis Generation (HG) and Hypothesis Verification (HV). In HG, feature point detection algorithm and mathematical morphology were used as the primary techniques, and the character texture and color information of vehicle license plate were extracted as the features to generate the candidates. In HV, gray projection technology and constant feature of vehicle license plate were used to verify the candidates from HG, then the correct license plate was located. The experimental results show that the proposed algorithm can achieve the localization success ratio of 96.6% and the precision of 95.4% in the testing image set in real environment. Moreover, the rationality and validity of the multi-feature fusion algorithm are verified.