NDLI: Binary-channel can covers defects detection system based on machine vision

Content Provider	IEEE Xplore Digital Library
Author	Bin Feng Shu-xia Guo Feng-ling Zhang Chao-jun Zhu Lei Wang
Copyright Year	2012
Description	Author affiliation: School of Physics and Mechanical & Electrical Engineering, Xiamen University, Xiamen, 361005, China (Bin Feng; Shu-xia Guo; Feng-ling Zhang; Chao-jun Zhu; Lei Wang)
Abstract	In order to realize the on-line detection and elimination of unqualified can covers, a binary-channel inspection system based on machine vision is proposed in this article. The system mainly consists of two illumination sources, two cameras, two sensors, an IPC, an interface circuit, two eliminating devices, a set of algorithms for image processing and a software for the control of independent binary-channel. In working status, each channel of the system is placed directly above a conveyor, which transports can covers to the detection position so that the camera is triggered, and then an image is captured with a flash. The cover images are transferred to the IPC and then processed by the algorithm that based on template matching and variation model. Depending on the processing results unqualified covers are eliminated. The system is proved to be non-pollution, low-cost, and defects such as double covers, no glue, shoulder scratch, distortion can be detected with a 98.7% accuracy and a speed of 1200 covers per minute.
Starting Page	1
Ending Page	4
File Size	455349
Page Count	4
File Format	PDF
ISBN	9781467321440
ISSN	21635048
e-ISBN	9781467321457
e-ISBN	9781467321433
DOI	10.1109/ICASID.2012.6325323
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-24
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image sensors image processing algorithms Machine vision Lighting Cameras binary-channel Software can covers defects detection Sensors machine vision
Content Type	Text
Resource Type	Article

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