Efficient Sampling for Computer Vision Inspection in Automated Quality Control

机器视觉检测系统即将成为 商业广告。 它们的最大优点是可以用来检查 对于人类来说过于复杂的零件的几何质量 督察 它们的最大缺点是速度太慢， 因为它们处理大量的数据和计算时间， 因此，太长了。 这项工作将使人们能够 这个问题，使用统计抽样，以减少，合理， 确保零件质量所需的数据量。 的工作范围 仅限于直线度和圆度的基本测量。 该方法是建立样本之间的数学关系， 尺寸和检查精度。 接下来，将建立关系 样本量和所选机器视觉的处理时间之间的关系 系统. 然后将建立标准，以选择适当的 抽样方法 这些将被放入容易参考的表格中， 质量保证从业人员。 样本量将以术语表示 测试部件上的扫描次数， 每次扫描。 检验精度将以尺寸表示 测量质量的置信区间（误差） 特色

Machine vision inspection systems are on the threshold of becoming commercial. Their great advantage is that they can be used to check the geometric quality of parts that are too complex for human inspectors. Their great disadvantage is that they are too slow, partly because they process vast amounts of data and computation times, therefore, are too long. This work will result in ways to get around this problem, using statistical sampling to reduce, rationally, the amount of data required to assure part quality. The scope of the work is restricted to the basic measurements of straightness and roundness. The approach is to develop a mathematical relationship between sample size and inspection accuracy. Next, a relationship will be established between sample size and processing time for selected machine vision systems. Then criteria will be established for selection of a proper sampling method. These will be put into forms easily referenced by quality assurance practitioners. Sample size will be stated in terms of number of scans across the test part, and number of points within each scan. Inspection accuracy will be expressed in terms of the size of the confidence interval (error) of the measured quality characteristics.