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Even if the same instrument or technology such as spectral analysis may be used for wood species and defect detection, the mutual disturbance exists so that the detection accuracy is low. In fact, wood species detection and wood defect detection are usually performed with different instruments or technologies. However, simultaneous investigations on wood species and wood defect detections are scarcely performed to make an objective wood quality assessment. combine the wavelet transform and neural networks to analyze and recognize different types and sizes of wood internal defects using an ultrasonic device. Researchers also have proposed some schemes for detecting wood internal defects by using X-rays, gamma rays, microwaves, and longitudinal stress waves. Īs 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). For example, Piuri and Scotti present a scheme for the wood species classification based on the analysis of fluorescence spectra. The more common schemes in the literature consider the vibration spectroscopy and the Raman spectroscopy. Recently, the wood spectral reflectance characteristics are also exploited for the species classification. 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 and its color analysis. 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. 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%. The color moments of scanned points are used for classification, but the defect points are not used. Finally, wood species identification is performed with the wood surface’s color information. The integration algorithm is used to calculate the surface area and volume of every defect. Second, a deep preferred search algorithm is used to segment the retained defect points marked with different colors. 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).
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Each 3D point contains its, , and coordinate and its RGB color information. First, an Artec 3D scanner is used to scan the wood surface to get the 3D point cloud. 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. Wood grading and wood price are mainly connected with the wood defect and wood species.