Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (4): 28-34.DOI: 10.3778/j.issn.1002-8331.2009-0476

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Review of Research on Machine Vision Defect Detection Based on Literature Measurement

PENG Zhaoyong, WU Quan, CHEN Huawei, ZHENG Yue, WANG Shuxiang   

  1. School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China
  • Online:2021-02-15 Published:2021-02-06



  1. 贵州师范大学 机械与电气工程学院,贵阳 550025


In order to systematically investigate and intuitively reflect the status quo, hotspots and frontiers of machine vision defect detection research in China, 588 related literatures from 1995 to 2020 are selected based on CNKI database for bibliometric measurement and knowledge map analysis. From 1995 to 2020, the research on machine vision defect detection in China has generally experienced three stages:preliminary exploration(1995-2005), rapid growth(2006-2011) and high-speed growth(2012-2020). Among the research keywords, machine vision, image processing, defect detection and support vector machine are the core words in this field. In keyword evolution analysis, the research is expanded from defect detection, image processing and deep learning focusing on machine vision to the subdivision fields such as surface defects, image segmentation and digital image processing. And WANG Yaonan, PENG Yu, FAN Tao and their team members are among the most prolific authors. The School of Automation of Guangdong University of Technology and the School of Manufacturing Science and Engineering of Sichuan University, plays important roles in academic research. In regard to journal carriers, a core group is formed of nine journals, which includs Progress [in] Laser [and] Optoelectronics  and Manufacturing Automation.

Key words: machine vision, defect detection, bibliometrics measurement, knowledge mapping, CiteSpace



关键词: 机器视觉, 缺陷检测, 文献计量, 知识图谱, CiteSpace