机器视觉论文：基于计算机视觉的柑橘分选系统

机器视觉论文：基于计算机视觉的柑橘分选系统【中文摘要】我国是一个柑橘出产大国,柑橘分选的速度与精确度直接影响到我国柑橘在国际市场上的竞争力。目前,我国柑橘分选还主要依靠人工完成,该方法不仅消耗了大量劳动力资源,而且分级精度不高、分级效率低。利用计算机视觉对水果进行分选是提高分选速度与精度的必然选择。基于计算机视觉的柑橘分选方法具有智能化程度高、分级精度高、速度快、成本低等优点,但其存在的主要问题是算法复杂、运算量大。本文主要研究包括颜色模型、图像滤波、图像分割、图像边界提取、最大和最小直径检测、图像缺陷检测以及系统的软硬件组成在内的整个柑橘分选系统。为了减少系统数据传输量,本系统使用YUV颜色模型；针对在极为复杂的背景下,一般分割算法不能快速并准确地将图像前景色与背景色分割开来的问题,本文提出了使用多个颜色通道的组合来分割图像的方法,提高了图像前景色与背景色准确分割速度。在图像预处理方面,为了减少运算量,缩短算法运行时间,本文在对各种方法进行分析后,使用计算量较小的快速中值滤波方法对图像进行滤波,并使用基于线段的边界提出方法来提取图像边界。针对原有的水果直径检测算法计算量过大以及计算精度不高的问题,本文提出了一种新的基于图像分割的直径检测方法,该方法能够在较短的时间内较为精确的检测出柑橘的直径。为了解决传统的缺陷检测算法运量大,运行时间长的问题,本文提出了阈值分割与区域增长相结合的缺陷检测方法,该方法能够快速、全面的将柑橘缺陷识别出来。本系统的软件部分在window XP系统上的VC+6.0中得到了实现,硬件部分也已搭建完成并已应用于实际工业生产中。实践证明,该系统能够快速、精确的对柑橘进行分级,系统性能达到了预期的设计目标。【英文摘要】China is a main country of producing oranges, the spead and the accuracy of the orange classification affects the competition of the oranges in the international market. At present, orange classification mainly depends on manual work in China, this method not only needs a lot of person to finish the job, but also has low accuracy and efficiency. Classifying the fruits by using computer vision is the inevitable choice to improve the speed and accuracy of fruit classification. The method of orange classification by machine vision is intelligent with high accuracy and speed and costs lowly, but the large amount of caculation and the complexity of the algorithm are the main problems of this method.This paper introduced the whole orange sortiong system which includes color models, image filtering, segment and edge extraction, the maximum and minimum diameter detection, the detection of image defection,the hardware and software of the system. YUV color model was used in this system to reduce the data quantity; the composition of several color channels was proposed to segment image to improve the problem that the foreground and background of the image can not be segmentted accurately and quickly when it comes to the very complex background. The method of rapid median filtering which has small amount of caculation was used to filter the image and the method of edge extraction by lines was chosen based on the comparation of different methods to reduce the program running time. A new method of diameter detection was proposed based on image segmentation to improve the problem that the old diameter detection algorithms have large amount of caculation and low accuracy. A new method of defection detection which used threshold segmentation combined with regional growth to segment images was proposed in this paper to resolve the problem that the traditional defection detection algorithms have large amount of caculation and long running time. Its proved that the new method was fast and accurate.The software and hardware of the system have been implemented, and the system was used in the actual industrial production. Its proved that the system could sort oranges rapidly and accurately. The expected design goal was achieved.【关键词】机器视觉 图像分割 直径检测 缺陷检测【英文关键词】machine vision image segmentation diameter detction defection detection【目录】基于计算机视觉的柑橘分选系统摘要4-5Abstract51 绪论10-171.1 柑橘分选的目的和意义10-111.1.1 我国柑橘生产现状10-111.1.2 柑橘分级的目的、意义111.2 柑橘分级方法11-121.2.1 人工分级11-121.2.2 机械分级121.2.3 基于计算机视觉的分级121.3 国内外研究现状12-151.3.1 国外的研究现状13-141.3.2 国内的研究现状14-151.4 本研究的主要内容15-161.5 本章小结16-172 颜色模型与图像滤波17-272.1 颜色模型17-212.1.1 RGB颜色模型17-182.1.2 HIS颜色模型18-192.1.3 YUV格式水果图像二值处理19-212.2 图像滤波21-242.2.1 均值滤波212.2.2 中值滤波21-222.2.3 快速中值滤波22-242.3 实验结果与分析24-252.4 结语25-273 图像分割与图像边界提取27-403.1 图像分割27-333.1.1 特征聚类法和基于神经网络的分割算法27-283.1.2 单通道阈值分割法28-313.1.3 多通道分割31-333.2 图像边界提取33-393.2.1 Soble算子333.2.2 Canny算子33-343.2.3 基于线段的边界提取方法34-383.2.4 实验结果与分析38-393.3 结语39-404 柑橘最大直径检测40-464.1 水果图像最大直径检测42-434.2 算法实现与比较43-454.3 结语45-465 柑橘缺陷检测46-535.1 定向技术47-485.2 阈值分割与区域增长结合分割法48-505.2.1 阈值分割方法495.2.2 基于区域的方法49-505.2.3 阈值分割与区域增长结合分割法505.3 实验结果与分析50-525.4 结语52-536 系统框架53-606.1 系统硬件构成53-556.1.1 计算机53-546.1.2 图像采集系统与照明系统54-556.1.3 传送装置556.1.4 分级执行装置556.2 软件部分55-596.2.1 上位机程序55-586.2.2 水果检测程序58-596.3 结语59-607 结论与展望60-62参考文献62-65个人简历65-66致谢66located in the Tomb, Dong