基于机器视觉的零件特征尺寸提取算法
发布时间：2018-10-30 19:55
【摘要】：目的复杂型面零件功能特征多样,结构尺寸呈现空间分布,传统的手工检测方法无法满足检测工作要求,为提升检测效率,提出一种基于机器视觉的非接触式测量方法。方法使用CCD相机采集图像信息,对图像进行分析处理,获得圆的亚像素边缘轮廓,再通过最小二乘法进行圆拟合求得圆的参数方程,最后利用几何距离公式求得像素距离。通过系统标定求出像素当量,由像素当量最终求得圆与圆之间的实际距离。结果最小二乘拟合圆亚像素边缘检测算法稳定,抗噪性能较好,算法的分辨率为0.001个像素。结论该方法可正确、方便、有效地对零件进行尺寸测量。
[Abstract]:In order to improve the detection efficiency, a non-contact measurement method based on machine vision is proposed. Methods the image information was collected by CCD camera, the image was analyzed and processed, the sub-pixel edge contour of the circle was obtained, then the circle parameter equation was obtained by the least square method, and the pixel distance was obtained by the geometric distance formula. The pixel equivalent is calculated by the system calibration, and the actual distance between the circle and the circle is obtained from the pixel equivalent. Results the edge detection algorithm of least square fitting circle subpixel is stable and robust. The resolution of the algorithm is 0.001 pixels. Conclusion this method is accurate, convenient and effective in measuring the dimensions of parts.
【作者单位】： 浙江大学宁波理工学院;宁波六和包装有限公司;
【基金】：国家自然科学基金(51075362) 宁波市鄞州区科技局区重大产业技术创新专项(2016G002)
【分类号】：TP391.41
[Abstract]:In order to improve the detection efficiency, a non-contact measurement method based on machine vision is proposed. Methods the image information was collected by CCD camera, the image was analyzed and processed, the sub-pixel edge contour of the circle was obtained, then the circle parameter equation was obtained by the least square method, and the pixel distance was obtained by the geometric distance formula. The pixel equivalent is calculated by the system calibration, and the actual distance between the circle and the circle is obtained from the pixel equivalent. Results the edge detection algorithm of least square fitting circle subpixel is stable and robust. The resolution of the algorithm is 0.001 pixels. Conclusion this method is accurate, convenient and effective in measuring the dimensions of parts.
【作者单位】： 浙江大学宁波理工学院;宁波六和包装有限公司;
【基金】：国家自然科学基金(51075362) 宁波市鄞州区科技局区重大产业技术创新专项(2016G002)
【分类号】：TP391.41
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