Abstract
Solid wood panels are widely used in the wood flooring and furniture industries, and paneling is an excellent material for indoor decoration. The classification of colors helps to improve the appearance of wood products assembled from multiple panels due to the differences in surface colors of solid wood panels. Traditional wood surface color classification mainly depends on workers’ visual observations, and manual color classification is prone to visual fatigue and quality instability. In order to reduce labor costs of sorting and to improve production efficiency, in this study, we introduced machine vision technology and an unsupervised learning technique. First-order color moments, second-order color moments, and color histogram peaks were selected to extract feature vectors and to realize data dimension reduction. The feature vector set was divided into different clusters by the K-means algorithm to achieve color classification and, thus, the solid wood panels with similar surface color were classified into one category. Furthermore, during twice clustering based on second-order color moment, texture recognition was realized on the basis of color classification. A sample of beech wood was selected as the research object, not only was color classification completed, but texture recognition was also realized. The experimental results verified the effectiveness of the technical proposal.
Highlights
IntroductionSolid wood panels are widely used in solid wood furniture [1], wood flooring, and other industries because of their gloss finish, good decoration performance, effective sound absorption, high strength, easy processing, durability, and long service life
The surface color classification of solid wood has mainly been based on manual observation, which is significantly influenced by human factors and has low efficiency and, cannot meet society’s needs in terms of processing automation and intelligence and human–computer interaction
By creating new datasets analyzing the effect of different color feature combinations, a new classification and analyzing the effect of different color feature combinations, a new classificationmethod for sorting of solid wood plates was was proposed, and texture recognition was method formulti-level multi‐levelcolor color sorting of solid wood plates proposed, and texture realized
Summary
Solid wood panels are widely used in solid wood furniture [1], wood flooring, and other industries because of their gloss finish, good decoration performance, effective sound absorption, high strength, easy processing, durability, and long service life. The surface parameters of solid wood panels include color [2], texture [3], gloss [4], roughness, deformation rate, planeness, etc., which are directly related to the visual beauty and decorative performance of wood products and are closely related to the quality evaluation of wood products. It is theoretically and practically important to achieve automatic and intelligent detection and sorting of solid wood panel colors. Color is an important surface characteristic parameter of solid wood panels, as well as an important index to evaluate the quality, grade, and market value of wood products. The surface color classification of solid wood has mainly been based on manual observation, which is significantly influenced by human factors and has low efficiency and, cannot meet society’s needs in terms of processing automation and intelligence and human–computer interaction. The emergence of new detection technologies that depend on machines overcomes the shortcomings of
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