【作者】 李振；

【导师】 傅圣雪；

【作者基本信息】 中国海洋大学 ， 通信与信息系统， 2008， 硕士

【摘要】 目前,采用计算机视觉技术是集装箱自动化装卸作业中实现集装箱识别和定位操作的发展方向。本文分析和探讨了两种集装箱检测方法的基本理论,搭建了集装箱装卸模型系统(Container Handling Model System--CHMS),在Windows环境下编写软件算法,并在CHMS系统上对分别对两种方法进行了仿真实验。实验表明本文提出的集装箱检测方法是合理的,具有一定的理论价值与实用价值。本文的研究工作主要包括以下几个方面:1.将基于AdaBoost的学习算法用于集装箱检测,从一个较大的特征集中选择少量关键的haar-like特征,产生一个高效的强分类器。再用级联方式将单个的强分类器级联成为一个更加复杂的级联分类器。对AdaBoost算法的收敛性能、泛化能力以及权重更新方法对分类器性能的影响等进行了深入的分析。本文使用了自己创建的集装箱模型样本库进行训练得到分类器,并在多种背景、光线条件下进行了集装箱模型检测实验。实验结果表明,得到的集装箱模型分类器效果理想。2.提出基于Kalman滤波的彩色模板更新的方法。介绍了基于色彩直方图的模板匹配方法和Kalman滤波的基本原理,给出了色彩直方图的具体算法。对模板内每一个像素值的三个颜色分量使用Kalman滤波进行更新,得到最优的模板图像,然后用模板匹配算法来检测集装箱。此方法与原先的固定模板匹配算法相比,提高了模板匹配的准确度和稳定度。更多还原

【Abstract】 Currently, using computer vision technology is the direction of container recognition and position in automatic container loading and unloading operations.Two of literatures, surveys and research papers concerning up-to-date techniques of container detection are read and analyzed. Putting up the Container Handling Model System(CHMS), compiling the code in Windows, and carrying out simulations about the two methods on the CHMS. The experiments indicate that the methods of container detection proposed in showing a certain degree of theoretical and practical value. Paper mainly includes the following several aspects:1.Using container detection method based on AdaBoost learning algorithm, which selects few key haar-like features from a large set of features, to build a robust cascade classifier. The convergence and generalization capability of AdaBoost algorithm as well as the effect of weight-updating approach on the classifier’s performance are analysed comprehensively. This paper used the sample storehouse which one founded to carry on the training and get the container classifier. Detecting the container model In a variety of backgrounds and lighting conditions used the classifier. The experimental results show that the effect of container classifier is suitable to detection.2. Providing an approach to update the template based on a Kalman filter. Intruducing the the basic principles of the template matching based on the color histogram and the Kalman filter, and giving the specific algorithm of the color histogram. The Kalman filter update three component of each pixel value and get the optimum template, and containers are detected by the optimum template. This method improves the accuracy and the stability of template matching.更多还原