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

Zhao Peng,Ning Xiao,Li Yue

doi:10.1155/2016/7049523

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 X, Y, and Z coordinate and its RGB color information. After preprocessing, the Z 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%.

Highlights

Wood species and wood defects are two key issues in the wood quality assessment so as to judge the physical property and commercial value of different wood products correctly [1]
The Artec 3D scanner is used to get the wood surface’s point cloud data, and subsequent defect segmentation and measurement are performed by use of C language programming
We have proposed a simultaneous wood defect and wood species detection scheme based on 3D scanning and signal processing

Summary

Introduction

Wood species and wood defects are two key issues in the wood quality assessment so as to judge the physical property and commercial value of different wood products (e.g., wood veneer, lumber, or board) correctly [1]. Some visual image characteristics have been used in the wood species recognition and can be divided into two general categories: wood surface’s texture analysis [2, 3] and its color analysis [4, 5]. The wood spectral reflectance characteristics are exploited for the species classification. Piuri and Scotti present a scheme for the wood species classification based on the analysis of fluorescence spectra [9]. As for the wood surface’s defect detection, the spectral analysis and laser scanning schemes are usually used to fulfill the qualitative detection on the wood external defects (e.g., cavity, worm tunnel, knots, or erosion) [10,11,12].

Methods

Results

Conclusion