提出了一种基于光学相干层析成像（OCT）技术实现人工心脏生物瓣膜三维缺陷检测的方法，发展了一种生物瓣膜表面边界拟合算法。根据拟合结果进行坐标变换，使瓣膜表面边界趋于水平但保留表面纤维束高度和异常起伏的高频变化。利用所提方法对人工心脏生物瓣膜三尖瓣支架和其中的瓣膜小叶进行成像实验，实现了高分辨率、大视场、实时三维结构成像，成像结果可以显示生物瓣膜纤维层、光滑层、层间缺陷以及切割缺陷。该技术有望被广泛应用于人工心脏生物瓣膜制造检测领域。

提出了一种基于空间像的极紫外光刻掩模相位型缺陷检测方法，用于检测多层膜相位型缺陷的类型、位置和表面形貌。缺陷的类型、位置和表面形貌均会影响含缺陷掩模的空间像的分布。因此，采用深度学习模型构建含缺陷掩模的空间像与待测缺陷信息之间的映射，利用训练后的模型可从含缺陷掩模的空间像中获取待测缺陷信息。采用卷积神经网络（CNN）模型构建含缺陷空白掩模的空间像和缺陷类型与位置之间的关系，建立用于缺陷类型和位置检测的CNN模型。在获取缺陷的类型与位置后，基于测得的缺陷位置对空间像进行截取，利用截取后的空间像的频谱信息和多层感知机模型获取缺陷表面形貌参数。仿真结果表明，所提方法可对多层膜相位型缺陷的类型、位置和表面形貌参数进行准确检测。

The growing demand for electronic devices, smart devices, and the Internet of Things constitutes the primary driving force for marching down the path of decreased critical dimension and increased circuit intricacy of integrated circuits. However, as sub-10 nm high-volume manufacturing is becoming the mainstream, there is greater awareness that defects introduced by original equipment manufacturer components impact yield and manufacturing costs. The identification, positioning, and classification of these defects, including random particles and systematic defects, are becoming more and more challenging at the 10 nm node and beyond. Very recently, the combination of conventional optical defect inspection with emerging techniques such as nanophotonics, optical vortices, computational imaging, quantitative phase imaging, and deep learning is giving the field a new possibility. Hence, it is extremely necessary to make a thorough review for disclosing new perspectives and exciting trends, on the foundation of former great reviews in the field of defect inspection methods. In this article, we give a comprehensive review of the emerging topics in the past decade with a focus on three specific areas: (a) the defect detectability evaluation, (b) the diverse optical inspection systems, and (c) the post-processing algorithms. We hope, this work can be of importance to both new entrants in the field and people who are seeking to use it in interdisciplinary work.

相对于接触式测量，机器视觉检测这种非接触式测量具有效率高、信息全、稳定性好、可识别缺陷等优点，在齿轮检测领域得到越来越广泛的应用。近十年来出现了影像仪、闪测仪、CVGM仪器、在线检测设备等多种基于机器视觉技术的齿轮检测仪器，它们既可以实现齿轮综合式测量，又可以实现齿轮分析式测量。回顾了齿轮视觉检测仪器的发展历程和特点，分析了齿轮视觉检测中边缘检测、亚像素定位、特征提取和模式识别等算法的研究和应用进展，总结了机器视觉在齿轮精度测量和齿轮缺陷检测两个方面的技术发展，并指明了齿轮视觉检测仪器与技术的发展前景。

Additive manufacturing (AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various internal defects, such as powder agglomeration, balling, porosity, internal cracks and thermal/internal stress, which can significantly affect the quality, mechanical properties and safety of final parts. Therefore, defect inspection methods are important for reducing manufactured defects and improving the surface quality and mechanical properties of AM components. This paper describes defect inspection technologies and their applications in AM processes. The architecture of defects in AM processes is reviewed. Traditional defect detection technology and the surface defect detection methods based on deep learning are summarized, and future aspects are suggested.

为建立完善的熔石英元件吸收缺陷检测体系, 有效提升熔石英元件的抗激光损伤阈值, 设计了一套熔石英损伤特性原位测量装置, 包括光热扫描测量与拉曼光谱测量两部分。实验表明, 该装置不仅可以对熔石英样品的定点损伤特性进行测量, 同时可以通过二维光热扫描测出样品的吸收特性和缺陷分布。此外, 通过该装置的显微拉曼光谱测量模块可实现在辐照过程中对缺陷所致拉曼带的原位测量, 获取激光辐照过程熔石英材料改性的信息, 对有效控制和评价熔石英激光损伤特性具有重要意义。

为了提高皮革缺陷检测效率, 提出了一种基于改进双边滤波的皮革缺陷检测算法。通过搭建机器视觉检测平台, 完成不同种类缺陷的皮革样本的图像采集, 采用改进的双边滤波算法处理样本图像, 模糊皮革背景纹理并保留缺陷边缘轮廓, 在此基础上, 计算各类缺陷的4种特征参量作为输入向量, 构建了最小二乘支持向量机自动识别模型。结果表明, 与聚类分析算法、阈值分割法和小波分析法相比, 本文中采用的算法能更高效地检测出皮革多种缺陷, 检测平均用时0.83s,缺陷检测准确率为93.3%。此研究结果为皮革的实时检测提供了有效途径。

注塑齿轮生产效率高、批量大, 而传统的人工检测效率低、精度差, 为确保产品质量和生产效率, 研发了一种面向生产现场的注塑齿轮快速分选检测系统。该系统耦合在生产线上, 利用传送带上料、玻璃转盘传送、气动装置分选; 利用工业相机采集被测齿轮图像, 采用机器视觉的方法测量齿顶圆直径、中孔直径、同心度等基本参数, 并与其公差范围进行对比, 判断产品质量; 通过图像分割、边缘提取、特征提取、缺陷判别等方法, 对缺齿、黑点、披锋等表面缺陷进行检测; 最后, 该系统根据检测结果进行良次品分选, 具有快速、在线、自动化、非接触等特点。试验结果表明, 该系统测量重复性≤0005 mm, 检测速度大于150个/min, 缺陷判别及分选功能准确无误, 且长时间运行稳定。该系统可以满足注塑齿轮生产现场大批量、高效率、高可靠的分选检测需求。