NIR-VGGNet19: A Novel Deep Convolutional Neural Network for Pinus NIR Spectra Classification

Abstract

Wood is an indispensable non-renewable resource and plays an important role in our life. So, it is a necessity to apply accurate classification techniques. In this paper, a novel model of deep one-dimensional convolutional neural network NIR-VGGNet19 is proposed to classify seven pius wood by combining near-infrared spectroscopy. NIR-VGGNet19 uses a deep convolutional neural network to automatically extract features, eliminate noise, and solve the problem of spectral overlapping peaks; initially eliminates random noise by adding $1\times 7$ convolutional kernels; then uses an attention mechanism to extract more accurate features. According to the results, when NIR-VGGNet19 was tested against other methods, NIR-VGGNet19 had the highest classification accuracy, achieving 98.41% on the test set. In contrast, the accuracy of LeNet, AlexNet, VGGNet-19, ResNet-34, back propagation neural network, and support vector machine were 70.95%, 92.54%, 93.02%, 96.67%, 76.19%, and 71.26%, respectively. Thus, it suggests that the NIR-VGGNet19 can improve the accuracy of classifying wood of the homogeneous genus.

Keywords

• Wood classification
• near infrared spectroscopy
• VGGNet-19
• SE module
• anti-noise