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Automatic Defect Detection in Spring Clamp Production via Machine Vision
Joint Authors
Zhu, Xia
Chen, Renwen
Zhang, Yulin
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-09
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
There is an increasing demand for automatic online detection system and computer vision plays a prominent role in this growing field.
In this paper, the automatic real-time detection system of the clamps based on machine vision is designed.
It hardware is composed of a specific light source, a laser sensor, an industrial camera, a computer, and a rejecting mechanism.
The camera starts to capture an image of the clamp once triggered by the laser sensor.
The image is then sent to the computer for defective judgment and location through gigabit Ethernet (GigE), after which the result will be sent to rejecting mechanism through RS485 and the unqualified ones will be removed.
Experiments on real-world images demonstrate that the pulse coupled neural network can extract the defect region and judge defect.
It can recognize any defect greater than 10 pixels under the speed of 2.8 clamps per second.
Segmentations of various clamp images are implemented with the proposed approach and the experimental results demonstrate its reliability and validity.
American Psychological Association (APA)
Zhu, Xia& Chen, Renwen& Zhang, Yulin. 2014. Automatic Defect Detection in Spring Clamp Production via Machine Vision. Abstract and Applied Analysis،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1013388
Modern Language Association (MLA)
Zhu, Xia…[et al.]. Automatic Defect Detection in Spring Clamp Production via Machine Vision. Abstract and Applied Analysis No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1013388
American Medical Association (AMA)
Zhu, Xia& Chen, Renwen& Zhang, Yulin. Automatic Defect Detection in Spring Clamp Production via Machine Vision. Abstract and Applied Analysis. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1013388
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1013388