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International Journal of Optics
Volume 2016 (2016), Article ID 7049523, 6 pages
http://dx.doi.org/10.1155/2016/7049523
Research Article

Simultaneous Wood Defect and Species Detection with 3D Laser Scanning Scheme

Information and Computer Engineering College, Northeast Forestry University, Harbin 150040, China

Received 18 September 2016; Accepted 22 November 2016

Academic Editor: Sulaiman Wadi Harun

Copyright © 2016 Zhao Peng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Wood grading and wood price are mainly connected with the wood defect and wood species. In this paper, a wood defect quantitative detection scheme and a wood species qualitative identification scheme are proposed simultaneously based on 3D laser scanning point cloud. First, an Artec 3D scanner is used to scan the wood surface to get the 3D point cloud. Each 3D point contains its , , and coordinate and its RGB color information. After preprocessing, the coordinate value of current point is compared with the set threshold to judge whether it is a defect point (i.e., cavity, worm tunnel, and crack). Second, a deep preferred search algorithm is used to segment the retained defect points marked with different colors. The integration algorithm is used to calculate the surface area and volume of every defect. Finally, wood species identification is performed with the wood surface’s color information. The color moments of scanned points are used for classification, but the defect points are not used. Experiments indicate that our scheme can accurately measure the surface areas and volumes of cavity, worm tunnel, and crack on wood surface with measurement error less than 5% and it can also reach a wood species recognition accuracy of 95%.