Liao Dahai, Yin Mingshuai, Luo Hongbin, Li Jun, Wu Nanxing
J Opt Soc Am A Opt Image Sci Vis. 2022 Apr 1;39(4):571-579. doi: 10.1364/JOSAA.449088.
Defect detection is a critical way to ensure quality for silicon-nitride-bearing rollers. To improve detection efficiency and precision for silicon-nitride-bearing roller surface defects, in this paper, a novel machine vision system for the detection of its surface defects is designed. This method combines image segmentation and wavelet fusion to extract features from an image. In turn, the features are used in a classifier based on the -nearest neighbor for defect classification. The optimized image segmentation algorithm that is combined with wavelet fusion is the innovation of the proposed method. It is evaluated using different defect images acquired by the machine vision system. Our experiments show that the proposed machine vision system's precision in anomaly detection of the silicon-nitride-bearing roller surface can achieve 98.5%; further, its classification precision of various defects is greater than 91.5%. It has resulted in a solution for the automatic identification of the silicon-nitride-bearing roller surface defects.
缺陷检测是确保含氮化硅滚筒质量的关键方法。为了提高含氮化硅滚筒表面缺陷的检测效率和精度，本文设计了一种用于检测其表面缺陷的新型机器视觉系统。该方法结合图像分割和小波融合从图像中提取特征。进而，这些特征被用于基于k近邻的分类器进行缺陷分类。与小波融合相结合的优化图像分割算法是该方法的创新之处。使用机器视觉系统采集的不同缺陷图像对其进行评估。我们的实验表明，所提出的机器视觉系统在含氮化硅滚筒表面异常检测中的精度可达98.5%；此外，其对各种缺陷的分类精度大于91.5%。它为含氮化硅滚筒表面缺陷的自动识别提供了一种解决方案。
