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A low-cost machine vision system for the recognition and sorting of small parts
Author(s): Gustavo Barea; Brian W. Surgenor; Vedang Chauhan; Keyur D. Joshi
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Paper Abstract

An automated machine vision-based system for the recognition and sorting of small parts was designed, assembled and tested. The system was developed to address a need to expose engineering students to the issues of machine vision and assembly automation technology, with readily available and relatively low-cost hardware and software. This paper outlines the design of the system and presents experimental performance results. Three different styles of plastic gears, together with three different styles of defective gears, were used to test the system. A pattern matching tool was used for part classification. Nine experiments were conducted to demonstrate the effects of changing various hardware and software parameters, including: conveyor speed, gear feed rate, classification, and identification score thresholds. It was found that the system could achieve a maximum system accuracy of 95% at a feed rate of 60 parts/min, for a given set of parameter settings. Future work will be looking at the effect of lighting.

Paper Details

Date Published: 13 April 2018
PDF: 5 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961O (13 April 2018); doi: 10.1117/12.2309957
Show Author Affiliations
Gustavo Barea, Queen's Univ. (Canada)
Brian W. Surgenor, Queen's Univ. (Canada)
Vedang Chauhan, Queen's Univ. (Canada)
Keyur D. Joshi, Queen's Univ. (Canada)

Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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