一种灰度化混合法在集装箱箱号识别中的运用
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摘要
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%。
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%.
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%.
引文
[1] 李定谢尔.基于计算机视觉的集装箱识别与定位关键技术研究[D].杭州:浙江工业大学,2017.
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[19] 殷青松,戴曙光.基于改进的小波阈值图像去噪算法研究[J].软件导刊,2018(1):89-91.
[20] 张坤,李珊珊,王晓红,等.基于机器视觉的铁路信号灯检测技术[J].河北工业科技,2019,36(2):115-121.
[21] 刘远远,冯鹏,龙邹荣,等.基于靶标区域分割的双目定位系统研究与实现[J].激光与光电子学进展,2018(5):208-215.
[22] 陈园园.基于改进BP网络算法的智能图像分割研究[J].长沙铁道学院学报(社会科学版),2013,14(4):212-213.
[23] 顾梅花,苏彬彬,王苗苗,等.彩色图像灰度化算法综述[J/OL].计算机应用研究,2019(5),http://www.arocmag.com/article/02-2019-05-064.html.
[2] 郑运平,常宜斌.一种新的灰度图像的快速矩计算算法[J].计算机学报,2017,40(11):2619-2634.
[3] IMPEDOVO S,OTTAVIANO L,OCCHINEGRO S.Optical character recognition:A survey[J].International Journal of Pattern Recognition and Artificial Intelligence,1991,5(2):1-24.
[4] GOOCH A A,OLSEN S C,Tumblin J,et al.Color2gray:Salience-preserving color removal[J].ACM Transactions on Graphics,2005,24(3):634-639.
[5] RASCHE K,GEIST R,WESTALL J.Re-coloring images for gamuts of lower dimension[J].Computer Graphics Forum,2005,24(3):423-432.
[6] GRUNDLAND M,DODGSON N A.Decolorize:Fast,contrast enhancing,color to grayscale conversion[J].Pattern Recognition,2007,40(11):2891-2896.
[7] ARCHAMBEAU C,DELANNAY N,VERLEYSEN M.Mixtures of robust probabilistic principal component analyzers[J].Neurocomputing,2008,71(7-9):1274-1282.
[8] SEO J W,KIM S D.Novel PCA-based color-to-gray image conversion[C]// Proceedings of IEEE International Conference on Image Processing.2013:2279-2283.
[9] KIM Y,JANG C,DEMOUTH J,et al.Robust color-to-gray via nonlinear global mapping[J].ACM Transactions on Graphics,2009,28(5):Article No.161.
[10] BALA R,ESCHBACH R.Spatial color-to-grayscale transform preserving chrominance edge information[C]// Proceedings of Color and Imaging Conference.2004:82-86.
[11] ZHU W,HU R,LIU L.Grey conversion via perceived-contrast[M].Berlin:Springer,2014:299-309.
[12] 卢红阳,刘且根,熊娇娇,等.基于最大加权投影求解的彩色图像灰度化对比度保留算法[J].自动化学报,2017,43(5):843-854.
[13] 田全,王蕴哲.基于图像灰度特征的人眼定位及状态识别[J].电脑知识与技术,2018,14(3):170-172.
[14] 战宇辰.基于机器视觉的道路及车辆检测技术研究[D].北京:北京工业大学,2016.
[15] 安俊峰,刘海东,潘雷,等.基于PCA和灰度直方图特征融合的交通标志的分类研究[J].公路,2017(4):179-185.
[16] HAN X,KWITT R,AYLWARD S,et al.Brain extraction from normal and pathological images:A joint PCA/image-reconstruction approach[J].NeuroImage,2017,176(1):431-445.
[17] DE SOUZA R S D,MAIO U,BIFFI V,et al.Robust PCA and MIC statistics of baryons in early minihaloes[J].Monthly Notices of the Royal Astronomical Society,2018,440(1):240-248.
[18] 许鸿奎,韩晓,曲怀敬.结合假边缘提取和直方图分析的图像灰度变换[J].光学精密工程,2017,25(4):1070-1076.
[19] 殷青松,戴曙光.基于改进的小波阈值图像去噪算法研究[J].软件导刊,2018(1):89-91.
[20] 张坤,李珊珊,王晓红,等.基于机器视觉的铁路信号灯检测技术[J].河北工业科技,2019,36(2):115-121.
[21] 刘远远,冯鹏,龙邹荣,等.基于靶标区域分割的双目定位系统研究与实现[J].激光与光电子学进展,2018(5):208-215.
[22] 陈园园.基于改进BP网络算法的智能图像分割研究[J].长沙铁道学院学报(社会科学版),2013,14(4):212-213.
[23] 顾梅花,苏彬彬,王苗苗,等.彩色图像灰度化算法综述[J/OL].计算机应用研究,2019(5),http://www.arocmag.com/article/02-2019-05-064.html.