基于机器视觉的划痕检测技术综述

Survey of Scratch Detection Technology Based on Machine Vision

在现阶段的智能制造过程中,精密产品及仪器表面的零划痕质量要求不断提高,基于机器视觉的划痕检测方法因其无损高精度的特点具有重要的研究意义。综述了基于机器视觉的划痕检测技术的发展现状,将目前主流的划痕检测方法分为手工设计特征方法和深度学习方法。基于手工设计特征的划痕检测方法包括灰度分布统计法、变换域法和高低维空间映射法,基于深度学习的划痕检测方法包括有监督学习方法和无监督学习方法,总结了每种方法的优缺点和适用场景,阐述了基于机器视觉的划痕检测技术的发展趋势。

In the current intelligent manufacturing process, the requirements for zero scratch quality of precision products and instrument surfaces are constantly improving. The scratch detection method based on machine vision shows important research significance because of its non-destructive and high-precision characteristics. This paper summarizes the development status of scratch detection technology based on machine vision and divides the current mainstream scratch detection methods into manual design features and deep learning methods. The scratch detection methods based on manual design features include gray distribution statistics, transform domain, and high-and low-dimensional space mapping methods. The scratch detection methods based on deep learning include supervised and unsupervised learning methods.The advantages of each method are summarized, disadvantages and application scenarios are described, and development trends of scratch detection technology based on machine vision are expounded.