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Pre-Analysis of Printed Circuit Boards Based On Multispectral Imaging for Vision Based Recognition of Electronics Waste

Authors: Florian Kleber, Martin Kampel


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: Recycling, Printed Circuit Boards, Multispectral Imaging, Electronic Waste, rare-earth elements

Digital Object Identifier (DOI):

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[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,, 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:
[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:
[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: