罗菁,董婷婷,宋丹,等.表面缺陷检测综述[J].计算机科学与探索,2014,8(9):1041-1048.
ISSN 1673-9418CODEN JKYTA8
Journal of Frontiers of Computer Science and Technology 1673-9418/2014/08(09)-1041-08doi:10.3778/j.issn.1673-9418.1405007E-mail:fcst@https://www.sodocs.net/doc/2018234271.html, https://www.sodocs.net/doc/2018234271.html, Tel:+86-10-89056056表面缺陷检测综述*
罗菁1,2,董婷婷1,2+,宋丹1,2,修春波1,2
1.天津工业大学电工电能新技术天津市重点实验室,天津300387
2.天津工业大学电气工程与自动化学院,天津300387
A Review on Surface Defect Detection
LUO Jing 1,2,DONG Tingting 1,2+,SONG Dan 1,2,XIU Chunbo 1,2
1.Key Laboratory of Advanced Electrical Engineering and Energy Technology,Tianjin Polytechnic University,Tianjin 300387,China
2.School of Electrical Engineering and Automation,Tianjin Polytechnic University,Tianjin 300387,China +Corresponding author:E-mail:dong1331047@16
https://www.sodocs.net/doc/2018234271.html,
LUO Jing,DONG Tingting,SONG Dan,et al.A review on surface defect detection.Journal of Frontiers of Computer Science and Technology,2014,8(9):1041-1048.
Abstract:Surface defect detection based on machine vision has been widely used in the various fields,which is impor-tant for ensuring the product quality during automatic production.However,there are some challenges on the sur-face inspection,such as low contrast,shape similarity between defect regions and non-defect regions,tiny defect detection,inspection speed and accuracy.So,this paper gives the recent advances in surface defect detection.The surface defect detection is categorized into three types:statistics,spectrum and model approach.Then this paper compares several typical approaches in detail,including feature extraction,detecting algorithms and the perfor-mance of the algorithms,and analyzes the effectiveness of the algorithms deeply.Finally,this paper summarizes the challenge and future trend.
Key words:surface detection;defect detection;defect classification;quality control
摘要:基于机器视觉的表面缺陷检测技术已经广泛地应用在视觉检测各个领域中,它是确保自动化生产中产品质量的一个非常重要的环节。然而表面缺陷检测技术仍然面临着缺陷和非缺陷区域之间的低对比度,噪*The National Natural Science Foundation of China under Grant Nos.61203302,51107088(国家自然科学基金);the Tianjin Research Program of Application Foundation and Advanced Technology under Grant No.14JCYBJC18900(天津市应用基础与前沿技术研究计划).Received 2014-04,Accepted 2014-06.
CNKI 网络优先出版:2014-06-30,
https://www.sodocs.net/doc/2018234271.html,/kcms/doi/10.3778/j.issn.1673-9418.1405007.html