Pre-Analysis of Printed Circuit Boards Based On Multispectral Imaging for Vision Based Recognition of Electronics Waste
The increasing demand of gallium, indium and
rare-earth elements for the production of electronics, e.g. solid
state-lighting, photovoltaics, integrated circuits, and liquid crystal
displays, will exceed the world-wide supply according to current
forecasts. Recycling systems to reclaim these materials are not yet in
place, which challenges the sustainability of these technologies. This
paper proposes a multispectral imaging system as a basis for a vision
based recognition system for valuable components of electronics
waste. Multispectral images intend to enhance the contrast of images
of printed circuit boards (single components, as well as labels) for
further analysis, such as optical character recognition and entire
printed circuit board recognition. The results show, that a higher
contrast is achieved in the near infrared compared to ultraviolett and
visible light.
[1] R. Moss, E. Tzimas, H. Kara, P. Willis, and J. Kooroshy, “Critical metals
in strategic energy technologies - assessing rare metals as supply-chain
bottlenecks in low-carbon energy technologies,” JRC scientific and
technical report, Tech. Rep., 2011.
[2] RECLAIM, http://www.re-claim.eu/, accessed 02.09.2014. Std., 2014.
[3] F. Kleber, C. Pramerdorfer, M. Kampel, B. Comanesco, and E. Stanciu,
“Chemical analysis and computer vision based recognition of electronics
waste,” in Going Green - Care Innovation, 2014.
[4] D. A. Cremers and L. J. Radziemski, Handbook of Laser-Induced
Breakdown Spectroscopy. Research Corporation Tucson, AZ, John
Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex
PO19 8SQ, England., 2006.
[5] E. Malamas, E. Petrakis, M. Zervakis, L. Petit, and J. Legat, “A survey
on industrial vision systems, applications and tools,” Image and Vision
Computing, vol. 21, no. 2, pp. 171–188, 2003.
[6] W.-Y. Wu and M.-J. W. C.-M. Liu, “Automated inspection of printed
circuit boards through machine vision,” Computers in Industry, vol. 28,
no. 2, pp. 103–111, 1996.
[7] C. Fischer and I. Kakoulli, “Multispectral and hyperspectral
imaging technologies in conservation: current research and potential
applications,” Reviews in Conservation, vol. 7, pp. 3—-16, 2006.
[8] M. Hain, J. Bartl, and V. Jacko, “Multispectral Analysis of Cultural
Heritage Artefacts,” in Measurement Science Review, vol. 3, 2003.
[9] R. L. Easton, K. Knox, and W. Christens-Barry, “Multispectral Imaging
of the Archimedes Palimpsest,” in 32nd Applied Image Pattern
Recognition Workshop, AIPR 2003. Washington, DC: IEEE Computer
Society, 2003, pp. 111–118. [10] F. Hollaus, F. Kleber, and R. Sablatnig, “Multi-spectral imaging of
historic handwritings.” in 40th Annual International Conference on
Computer Applications and Quantitative Methods in Archaeology -
CAA2012, 2002, p. to appear.
[11] F. Kleber, M. Lettner, M. Diem, M. Vill, R. Sablatnig, H. Miklas,
and M. Gau, “Multispectral Acquisition and Analysis of Ancient
Documents,” in Conference on Virtual Systems and MultiMedia
(VSMM’08), Dedicated to Cultural Heritage - Project Papers, 2008,
pp. 184–191.
[12] F. Mairinger, Strahlenuntersuchung an Kunstwerken. Berlin: E.A.
Seemann, 2003.
[13] M. Moganti, F. Ercal, C. H. Dagli, and S. Tsunekawa, “Automatic
PCB Inspection Algorithms: A Survey,” 1Computer Vision and Image
Understanding, vol. 63, no. 2, pp. 287–313, 1996.
[14] R. Liu, Y. Shi, W. Kosonocky, and F. Higgins, “Infrared solder
joint inspection on surface mount printed circuit boards,” in
38th Midwest Symposium on Circuits and Systems. Proceedings,
vol. 1. IEEE, 1995, pp. 145–148. (Online). Available:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=504399
[15] Y. Hara, H. Doi, K. Karasaki, and T. Iida, “A system
for PCB automated inspection using fluorescent light,” IEEE
Transactions on Pattern Analysis and Machine Intelligence,
vol. 10, no. 1, pp. 69–78, 1988. (Online). Available:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=3868
[16] A. Ibrahim, T. Shoji, and T. Horiuchi, “Unsupervised Material
Classification of Printed Circuit Boards Using Dimension-Reduced
Spectral Information,” in Conference on Machine Vision Applications,
2009, pp. 13–16.
[17] A. Ibrahim, S. Tominaga, and T. Horiuchi, “Material Classification for
Printed Circuit Boards by Spectral Imaging System,” Lecture Notes in
Computer Science, vol. 5646, pp. 216–225, 2009.
[18] S. Tominaga, “Region segmentation by multispectral
imaging,” in Proceedings Fifth IEEE Southwest
Symposium on Image Analysis and Interpretation. IEEE
Comput. Soc, 2002, pp. 238–242. (Online). Available:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=999925
[1] R. Moss, E. Tzimas, H. Kara, P. Willis, and J. Kooroshy, “Critical metals
in strategic energy technologies - assessing rare metals as supply-chain
bottlenecks in low-carbon energy technologies,” JRC scientific and
technical report, Tech. Rep., 2011.
[2] RECLAIM, http://www.re-claim.eu/, accessed 02.09.2014. Std., 2014.
[3] F. Kleber, C. Pramerdorfer, M. Kampel, B. Comanesco, and E. Stanciu,
“Chemical analysis and computer vision based recognition of electronics
waste,” in Going Green - Care Innovation, 2014.
[4] D. A. Cremers and L. J. Radziemski, Handbook of Laser-Induced
Breakdown Spectroscopy. Research Corporation Tucson, AZ, John
Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex
PO19 8SQ, England., 2006.
[5] E. Malamas, E. Petrakis, M. Zervakis, L. Petit, and J. Legat, “A survey
on industrial vision systems, applications and tools,” Image and Vision
Computing, vol. 21, no. 2, pp. 171–188, 2003.
[6] W.-Y. Wu and M.-J. W. C.-M. Liu, “Automated inspection of printed
circuit boards through machine vision,” Computers in Industry, vol. 28,
no. 2, pp. 103–111, 1996.
[7] C. Fischer and I. Kakoulli, “Multispectral and hyperspectral
imaging technologies in conservation: current research and potential
applications,” Reviews in Conservation, vol. 7, pp. 3—-16, 2006.
[8] M. Hain, J. Bartl, and V. Jacko, “Multispectral Analysis of Cultural
Heritage Artefacts,” in Measurement Science Review, vol. 3, 2003.
[9] R. L. Easton, K. Knox, and W. Christens-Barry, “Multispectral Imaging
of the Archimedes Palimpsest,” in 32nd Applied Image Pattern
Recognition Workshop, AIPR 2003. Washington, DC: IEEE Computer
Society, 2003, pp. 111–118. [10] F. Hollaus, F. Kleber, and R. Sablatnig, “Multi-spectral imaging of
historic handwritings.” in 40th Annual International Conference on
Computer Applications and Quantitative Methods in Archaeology -
CAA2012, 2002, p. to appear.
[11] F. Kleber, M. Lettner, M. Diem, M. Vill, R. Sablatnig, H. Miklas,
and M. Gau, “Multispectral Acquisition and Analysis of Ancient
Documents,” in Conference on Virtual Systems and MultiMedia
(VSMM’08), Dedicated to Cultural Heritage - Project Papers, 2008,
pp. 184–191.
[12] F. Mairinger, Strahlenuntersuchung an Kunstwerken. Berlin: E.A.
Seemann, 2003.
[13] M. Moganti, F. Ercal, C. H. Dagli, and S. Tsunekawa, “Automatic
PCB Inspection Algorithms: A Survey,” 1Computer Vision and Image
Understanding, vol. 63, no. 2, pp. 287–313, 1996.
[14] R. Liu, Y. Shi, W. Kosonocky, and F. Higgins, “Infrared solder
joint inspection on surface mount printed circuit boards,” in
38th Midwest Symposium on Circuits and Systems. Proceedings,
vol. 1. IEEE, 1995, pp. 145–148. (Online). Available:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=504399
[15] Y. Hara, H. Doi, K. Karasaki, and T. Iida, “A system
for PCB automated inspection using fluorescent light,” IEEE
Transactions on Pattern Analysis and Machine Intelligence,
vol. 10, no. 1, pp. 69–78, 1988. (Online). Available:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=3868
[16] A. Ibrahim, T. Shoji, and T. Horiuchi, “Unsupervised Material
Classification of Printed Circuit Boards Using Dimension-Reduced
Spectral Information,” in Conference on Machine Vision Applications,
2009, pp. 13–16.
[17] A. Ibrahim, S. Tominaga, and T. Horiuchi, “Material Classification for
Printed Circuit Boards by Spectral Imaging System,” Lecture Notes in
Computer Science, vol. 5646, pp. 216–225, 2009.
[18] S. Tominaga, “Region segmentation by multispectral
imaging,” in Proceedings Fifth IEEE Southwest
Symposium on Image Analysis and Interpretation. IEEE
Comput. Soc, 2002, pp. 238–242. (Online). Available:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=999925
@article{"International Journal of Information, Control and Computer Sciences:69066", author = "Florian Kleber and Martin Kampel", title = "Pre-Analysis of Printed Circuit Boards Based On Multispectral Imaging for Vision Based Recognition of Electronics Waste", abstract = "The increasing demand of gallium, indium and
rare-earth elements for the production of electronics, e.g. solid
state-lighting, photovoltaics, integrated circuits, and liquid crystal
displays, will exceed the world-wide supply according to current
forecasts. Recycling systems to reclaim these materials are not yet in
place, which challenges the sustainability of these technologies. This
paper proposes a multispectral imaging system as a basis for a vision
based recognition system for valuable components of electronics
waste. Multispectral images intend to enhance the contrast of images
of printed circuit boards (single components, as well as labels) for
further analysis, such as optical character recognition and entire
printed circuit board recognition. The results show, that a higher
contrast is achieved in the near infrared compared to ultraviolett and
visible light.
", keywords = "Electronic Waste, Recycling, Multispectral Imaging,
Printed Circuit Boards, Rare-Earth Elements.", volume = "9", number = "3", pages = "656-5", }
