A surface defect identification method based on improved threshold segmentation algorithm
For enterprises, defect detection is very important because it is related to the quality of products produced by enterprises. With the development of machine vision, accurate analysis of image data benefits defect detection. In an enterprise that produces electronic cigarettes, professional and tech...
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| Veröffentlicht in: | Journal of physics. Conference series Jg. 1651; H. 1; S. 12072 - 12076 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Bristol
IOP Publishing
01.11.2020
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| ISSN: | 1742-6588, 1742-6596 |
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| Abstract | For enterprises, defect detection is very important because it is related to the quality of products produced by enterprises. With the development of machine vision, accurate analysis of image data benefits defect detection. In an enterprise that produces electronic cigarettes, professional and technical personnel used to detect a defect in a workpiece by using manual testing. The defect detection rate of this method is only 95%, and the efficiency is low. We use an improved threshold segmentation method to solve this problem in this paper and we have achieved success. Compared with typical methods, the accuracy of our proposed algorithm reaches over 99% and at the same time, the detection efficiency has been improved by more than 50%. Our method also has the advantage of simplicity, practicality and low cost. |
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| AbstractList | For enterprises, defect detection is very important because it is related to the quality of products produced by enterprises. With the development of machine vision, accurate analysis of image data benefits defect detection. In an enterprise that produces electronic cigarettes, professional and technical personnel used to detect a defect in a workpiece by using manual testing. The defect detection rate of this method is only 95%, and the efficiency is low. We use an improved threshold segmentation method to solve this problem in this paper and we have achieved success. Compared with typical methods, the accuracy of our proposed algorithm reaches over 99% and at the same time, the detection efficiency has been improved by more than 50%. Our method also has the advantage of simplicity, practicality and low cost. |
| Author | Gao, Xianwen Feng, Xinglong Luo, Ling |
| Author_xml | – sequence: 1 givenname: Xinglong surname: Feng fullname: Feng, Xinglong email: 1610238@stu.neu.edu.cn, fengxinglong@vip.163.com organization: Northeastern University, College of Information Science and Engineering , , China – sequence: 2 givenname: Xianwen surname: Gao fullname: Gao, Xianwen organization: Northeastern University, College of Information Science and Engineering , , China – sequence: 3 givenname: Ling surname: Luo fullname: Luo, Ling organization: Northeastern University, College of Information Science and Engineering , , China |
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| Cites_doi | 10.1016/j.mfglet.2017.12.001 10.1109/TCPMT.2018.2794540 10.1016/j.autcon.2018.08.006 10.1016/j.cad.2013.04.005 10.3788/OPE.20172506.1418 |
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| References | Yuan (JPCS_1651_1_012072bib1) 2017; 38 Galan (JPCS_1651_1_012072bib6) 2017; 15 Li (JPCS_1651_1_012072bib7) 2018; XX Zhu (JPCS_1651_1_012072bib3) 2017; 37 Jiang (JPCS_1651_1_012072bib9) 2013; 45 Yuan (JPCS_1651_1_012072bib5) 2014; 26 Cheng (JPCS_1651_1_012072bib10) 2018; 95 Tao (JPCS_1651_1_012072bib8) 2018; 8 Lin (JPCS_1651_1_012072bib2) 2017; 25 Wu (JPCS_1651_1_012072bib4) 2015; 36 |
| References_xml | – volume: 15 start-page: 5 year: 2017 ident: JPCS_1651_1_012072bib6 article-title: Surface defect identificaion and measurement for metal castings by vision system publication-title: Manufacturing Letters doi: 10.1016/j.mfglet.2017.12.001 – volume: 8 start-page: 689 year: 2018 ident: JPCS_1651_1_012072bib8 article-title: Wire Defect Recognition of Spring-Wire Socket Using Multitask Convolutional Neural Networks publication-title: Ieee Transactions on Components Packaging and Manufacturing Technology doi: 10.1109/TCPMT.2018.2794540 – volume: 95 start-page: 1081 year: 2018 ident: JPCS_1651_1_012072bib10 article-title: Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques publication-title: Automation in Construction doi: 10.1016/j.autcon.2018.08.006 – volume: 37 start-page: 937 year: 2017 ident: JPCS_1651_1_012072bib3 article-title: A on-line detection system development based on image pro-cessing for rubber hose defects publication-title: Transactions of Beijing Institute of Technology – volume: 26 start-page: 800 year: 2014 ident: JPCS_1651_1_012072bib5 article-title: Improved Image Preprocessing Algorithm for Rail Surface Defects Detection publication-title: Journal of Computer-Aided Design & Computer Graphics – volume: 45 start-page: 155 year: 2013 ident: JPCS_1651_1_012072bib9 article-title: A feature-based method of rapidly detecting global exact symmetries in CAD models publication-title: Computer-Aided Design doi: 10.1016/j.cad.2013.04.005 – volume: 36 start-page: 2258 year: 2015 ident: JPCS_1651_1_012072bib4 article-title: In-pipe internal defect inspection method based on active stereo omni-directional vision sensor publication-title: Chinese Journal of Scientific Instrument – volume: 25 start-page: 1418 year: 2017 ident: JPCS_1651_1_012072bib2 article-title: Evaluation of imaging performance for electroluminescence defect detector publication-title: Optics and precision engineering doi: 10.3788/OPE.20172506.1418 – volume: XX start-page: X year: 2018 ident: JPCS_1651_1_012072bib7 article-title: Review of development and application of defect detection technology publication-title: ACTA AUTOMATICA SINICA – volume: 38 start-page: 3100 year: 2017 ident: JPCS_1651_1_012072bib1 article-title: Review of tunnel lining crack detction algorithm based on machine vision publication-title: Chinese Journal of Scientific Instrument |
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| SubjectTerms | Algorithms Identification methods Image segmentation Machine vision Physics Surface defects Workpieces |
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