一种灰度化混合法在集装箱箱号识别中的运用

研究一种基于机器视觉的集装箱箱号识别方法。对于集装箱彩色图像预处理过程中的灰度化方法,传统的灰度化算法不能有效弥补图像中污损或其他信息缺失的部分,因此,本文提出使用主成分分析法(PCA)结合贝叶斯阈值估计灰度变化率的混合法对图像的灰度化进行优化,可以在判断图像中某一点灰度值与周围相邻像素点的灰度值的变化率后,弥补缺失信息,有效确定边缘特征,从而使后续的字符识别准确率大大提高。最后使用该算法模型设计实现一套用于港口集装箱的智能检测系统。经过Matlab实验验证,在对50幅港口集装箱箱号图像的识别中,通过使用本文提出的混合灰度化方法,与普通的均值法和加权平均法的灰度化方法相比,准确率更高,其中单一字符准确率可达96%,箱号准确率可达92%。

Application of Gray-level Hybrid Method in Recognition of Container Number

A recognition method of container number based on machine vision is studied. For the gray level method in the process of color image preprocessing of containers, the traditional grays cale algorithm can not effectively make up for the defilement or other missing information in the image. So, a hybrid method combining principal component analysis (PCA) with the gray change rate of Bayes threshold estimation is proposed to optimize the gray level of the image. It can make up for the missing information and effectively determine the edge features after judging the change rate of the gray value of a certain point in the image and the gray value of the neighboring pixel points, thus greatly improving the character recognition accuracy of the subsequent sequence. Finally, a set of intelligent detection system for port container is designed and implemented by using the algorithm model. Through the Matlab test for the identification of 50 port container number images, compared with the ordinary mean method and the weighted average method, the accuracy is better by using the mixed gray method proposed in this paper, and the accuracy rate of the single character can reach 96%, the accuracy rate of the container number recognition can reach 92%.