基于贝叶斯神经网络的近红外光谱实木地板表面缺陷检测

doi:10.3964/j.issn.1000-0593(2017)07-2041-05

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Abstract Surface defects of solid wood boards directly affect their mechanical properties and product grade, therefore, to achieve rapid detection of surface defects has important practical significance for online sort of solid wood boards. In view of the low recognition rate of the surface defect of the solid wood boards, a new method for the detection of 900~1 900 nm was proposed.by using a portable near infrared spectrometer First of all, the experiment collected absorption spectra of 180 samples with size of 200 mm×10 mm×10 mm, consisting of 60 samples with live knots, 60 samples with dead knots and 60 defect-free samples. Half of the samples were selected randomly as the training set, and the rest of samples were test set. Secondly, the the collecting NIR spectra of solid wood boards were preprocessed with Gaussian smoothing filter, piecewise multiplicative scatter correction and De-trending to reduce the spectral noise and eliminate the influence of the scattering spectrum; Afterwards, the improved genetic algorithm was utilized to select characteristic waves from the processed spectrum for building a model of defects recognition and classification; Finally, a model for recognizing and classifying the defects of solid wood boards was built through the improved neural network based on Bayesian neural network. The experiments used three types, containing live knots, dead knots and defects free, of solid wood board samples to train and test the model, the results showed that the model of based on Bayesian neural network was able to accurately identify three kinds of them, and the recognition rates were 92.20%, 94.47% and 95.57%, respectively. This study demonstrates that the type of solid wood boards surface defects with its near-infrared absorption spectra are closely related, and the article provides a rapid approach to achieve the accurate positioning of solid wood board defects which is as the next step.

Key words： Near-infrared spectra Solid wood boards Piecewise multiplicative scatter correction Improved genetic algorithm Bayesian neural network

Received: 2016-01-26 Accepted: 2016-04-21

ZTFLH:

S781.5

Corresponding Authors: ZHANG Yi-zhuo E-mail: nefuzyz@163.com

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2017)07-2041-05 OR http://www.gpxygpfx.com/EN/Y2017/V37/I07/2041