摘要:
惯性约束聚变试验中,对大批量的聚变靶球的表面质量进行检测和分类是一项重要的工作。传统的人工检测分类方法效率低,精度差,难以满足实际需要。提出了一种基于计算机视觉的缺陷检测及分类方法。该方法在获取待测微球的显微图像之后,通过设置兴趣区域提取圆内部的像素点,并以此绘制灰度直方图。然后计算其累积分布函数,经归一化处理之后对分布函数进行分段线性拟合。最终根据拟合后的分布函数,提出均匀性和透光性两个参数用于定量表示微球的表面质量,很好地实现了光滑、粗糙和畸形三种类型的微球的分类。实验结果表明,该检测分类方法的准确率均在90%以上,处理1280960分辨率的包含20个微球的图像平均只需300 ms,准确高效,可扩展性强。
Abstract:
The detection and classification of a large number of microspheres is a very important step in inertial confinement fusion experiments. The traditional manual detection and classification method has low efficiency and poor precision, which is difficult to meet the actual needs. This paper proposes a new algorithm of defect detection and classification based on computer vision. After obtaining the image of the microspheres to be measured, the gray histogram is drawn with the inner pixels extracted from the region of interest. Then the cumulative distribution function is calculated, normalized and fitted piecewise and linearly. According to the distribution function after fitting, two parameters, homogeneity and transparency, are proposed to quantitatively express the surface quality of microspheres, and the classification of three types of microspheres, which are smooth, rough and malformed, can be realized. The experimental results show that the accuracy of the proposed algorithm is over 90%. It only takes 300 ms to process an image about 20 microspheres with 1280960 resolution, which is accurate, efficient and extensible.
百度学术
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