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基于机器视觉的小型工件尺寸测量系统研究

发布时间:2019-03-05 15:30
【摘要】:随着机械加工行业自动化发展进程的不断推进,传统的测量技术已不能充分适应当前的发展需求,机械加工行业对加工工件的尺寸测量也提出了更高的要求。本文所研究的基于机器视觉的小型工件测量系统,主要解决人工测量速度慢,长时间测量易疲劳,测量精度不高,以及难以实现测量自动化等问题。本文首先根据课题需要确定系统硬件方案,分析研究相机、镜头、光源等主要硬件的工作原理。为了突出被测工件的轮廓边界,同时避免金属工件表面的反光,本系统选用了 LED环形无影光源照亮工件的轮廓。另外确定了相机、镜头等硬件设备的具体型号,构建了测量系统的整体硬件平台,完成了相机标定和被测工件的图像拍摄工作。利用Matlab 8.0进行程序开发,整体流程分为五个主要部分。一是图像滤波,经过对比分析,使用了中值滤波算法,在不破坏工件边缘信息的前提下较好的滤出了图像中的部分噪声。二是图像二值化处理,本系统采用了直方图阈值法确定阈值,将图像的边缘和背景清晰的区分开来。三是图像边缘提取,本系统采用了 Canny算子进行边缘提取,有效的提取出了图像的边缘信息,包括工件真实的边缘信息和噪声的边缘信息。四是噪声边缘地消除,在这部分中本文提出了提出了二值图像连续亮点区域聚合算法。该算法先将各个边缘像素点,包括噪声边缘像素点和工件有效边缘像素点,分别聚合在各自集合中,再跟据噪声边缘和工件有效边缘之间的像素个数差别,判断并消除噪声边缘点,最终实现了工件真实边缘的有效提取。五是边缘分段拟合,本文提出了映射分段算法,该算法利用图像拐点前后点的坐标斜率差值较大的特点来寻找边缘的拐点,准确有效的实现了连续边缘的分段。最后本文所涉及到的边主要是直线和圆弧,直接用直线和圆对各边进行拟合,再求取各条边的长度,完成测量工作。从实验结果可以看出,本文所设计组建的基于机器视觉的小型工件测量系统能够实现实时,自动化,高精度测量。
[Abstract]:With the development of automation in machining industry, the traditional measurement technology can no longer fully adapt to the current development needs, and the machining industry also put forward higher requirements for the size measurement of machined workpiece. The small workpiece measurement system based on machine vision studied in this paper mainly solves the problems of slow manual measurement, easy fatigue in long time measurement, low measurement precision, and difficulty to realize measurement automation and so on. In this paper, the hardware scheme of the system is determined according to the need of the project, and the working principle of the main hardware such as camera, lens and light source is analyzed. In order to highlight the contour boundary of the measured workpiece and avoid the reflection of the surface of the metal workpiece, the LED ring shadowless light source is selected to illuminate the contour of the workpiece. In addition, the specific models of the camera, lens and other hardware equipment are determined, the whole hardware platform of the measurement system is constructed, and the camera calibration and the image photographing of the measured workpiece are completed. Using Matlab 8.0 to develop the program, the whole process is divided into five main parts. The first is image filtering, after comparison and analysis, the median filtering algorithm is used to filter the partial noise in the image without destroying the edge information of the workpiece. The second is image binarization. In this system, histogram threshold method is used to determine the threshold value, and the edge and background of the image are clearly distinguished. Third, image edge extraction, the system uses the Canny operator for edge extraction, effective extraction of image edge information, including the real edge information of the workpiece and noise edge information. Fourth, the edge of noise is eliminated. In this part, a binary image continuous bright spot region aggregation algorithm is proposed. Firstly, each edge pixel, including noise edge pixel and workpiece effective edge pixel, is aggregated in each set, and then the number of pixels between the noise edge and the effective edge of the workpiece is different from the number of pixels between the noise edge and the effective edge of the workpiece. Finally, the real edge of the workpiece can be extracted effectively by judging and eliminating the noise edge points. Fifth, the edge segment fitting, this paper proposes a mapping segmentation algorithm, this algorithm uses the image inflection point before and after the coordinates of the slope difference is large to find the edge of the inflection point, accurate and effective realization of the continuous edge segmentation. Finally, the edges involved in this paper are mainly straight lines and arcs, which are fitted directly by straight lines and circles, and then the length of each edge is obtained to complete the measurement work. From the experimental results, it can be seen that the small workpiece measurement system based on machine vision designed in this paper can realize real-time, automatic and high-precision measurement.
【学位授予单位】:西南交通大学
【学位级别】:硕士
【学位授予年份】:2017
【分类号】:TG806;TP391.41

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