Empty bottle texture area defect detection based on POBT

doi:10.13382/j.jemi.2017.04.009

Home > Archive>Volume 31, Issue 4, 2017 >549-558. DOI:10.13382/j.jemi.2017.04.009

Empty bottle texture area defect detection based on POBT
DOI:
                        10.13382/j.jemi.2017.04.009
                    
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Author:
                        Zheng Yexin1Zheng Yexin
National Engineering Laboratory for Robot Visual Perception and Control Technology, College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;
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Wang Yaonan1Wang Yaonan
National Engineering Laboratory for Robot Visual Perception and Control Technology, College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;
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Zhou Xian′en1Zhou Xian′en
National Engineering Laboratory for Robot Visual Perception and Control Technology, College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;
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Jiang Xiaoxiao1Jiang Xiaoxiao
National Engineering Laboratory for Robot Visual Perception and Control Technology, College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;
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Peng Yu1Peng Yu
National Engineering Laboratory for Robot Visual Perception and Control Technology, College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;
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Liu Yuanqiang2Liu Yuanqiang
Foshan Xiangde Intelligent Technology Co.Ltd.,Foshan 528000,China
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Affiliation:1. National Engineering Laboratory for Robot Visual Perception and Control Technology, College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; 2. Foshan Xiangde Intelligent Technology Co.Ltd.,Foshan 528000,China
Clc Number:TP391;TN06
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Abstract:

To solve the problem that the existing glass bottle detection algorithm only aimed at the smooth surface with no pattern, a glass bottle texture area detection algorithm based on the POBT (phase only based transition) is proposed in the paper. The method first calculates the glass bottle center axis according to the bottle body gray projection, then obtains defect image by removing all the patterns through POBT, which including the process of nomalization in frequency domain to eliminate low frequency component of the glass texture area. At last, it extracts defects by using adaptive probability refined thresholding segmentation on the POBT image and compares the segmentation method with three conventional methods. The experiment result shows that aimed to the glass bottle glass texture area, the method can achieve highprecision detection with rapid speed of the glass bottle texture area.

Key words:machine vision; glass bottle; texture area defect detection; POBT; adaptive thresholding

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Online: July 26,2017
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