Top > IJAT > Development of Ultralow-Cost Machine Vision System

single-au.php

IJAT Vol.11 No.4 pp. 629-637
doi: 10.20965/ijat.2017.p0629
(2017)

Development Report:

Views over last 60 days: 729

Development of Ultralow-Cost Machine Vision System

Kenichi Endo†, Teruyuki Ishiwata, and Tomohiro Yamazaki

Automation Equipment Engineering Section, Nissan Motor Co., Ltd.
1 Natsushima, Yokosuka, Kanagawa 237-8523, Japan

†Corresponding author

Received:
November 1, 2016
Accepted:
April 11, 2017
Online released:
June 29, 2017
Published:
July 5, 2017
Creative Commons License
Keywords:
computer vision, machine vision, robot vision, embedded system, OpenCV
Abstract
This paper reports on the development of a low-cost machine vision inspection system to promote the wide employment of the system and foster further quality improvements in automobile manufacturing. The machine vision system consists of a camera that takes images of an inspection target, lighting to ensure appropriate illuminance, and a controller that analyzes the images and gives inspection results. By optimizing the performance and using free software, we succeeded in the development of an ultralow-cost machine vision system for one tenth of the cost of commercially available factory automation machine vision systems. The development and results are introduced in this paper. The applicability of the ultralow-cost machine vision system platform to applications other than inspection is also discussed.
Cite this article as:
K. Endo, T. Ishiwata, and T. Yamazaki, “Development of Ultralow-Cost Machine Vision System,” Int. J. Automation Technol., Vol.11 No.4, pp. 629-637, 2017.
Data files:
References
  1. [1] A. Abeta, “The Multiple Layer Architecture with High Portable in Embedded Software,” Information Processing Society of Japan, pp. 117-122, 2003.
  2. [2] M. Shimizu and M. Okutomi, “Sub-pixel estimation error cancellation on area-based matching,” Int. J. of Computer Vision, Vol.63, No.3, pp. 207-224, 2005.
  3. [3] M. Shimizu and M. Okutomi, “Multi-parameter simultaneous estimation on area-based matching,” Int. J. of Computer Vision, Vol.67, No.3, pp. 327-342, 2006.
  4. [4] G. Bradski and A. Kaehler, “Learning OpenCV Computer Vision with the OpenCV Library,” O’Reilly Media, 2008.
  5. [5] K. Kobayashi, H. Sasaki, T. Aoki, M. Kawamata, and T. Higuchi, “Rotatien Measurements using Rotation lnvariamt Phase Only Correlation,” ITE Technical Report, Vol.22, No.45, pp. 55-60, 1998.
  6. [6] K. Kobayashi, H. Nakajima, T. Aoki, M. Kawamata, and T. Higuchi, “Principles of Phase Only Correlation and Its Applications,” ITE Technical Report, Vol.20, No.41, pp. 1-6, 1996.
  7. [7] G. Borgefors, “Hierarchical Chamfer Matching:A Parametric Edge Matching,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.10, No.6, pp. 849-865, 1988.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024