BO-densenet: A bilinear one-dimensional densenet network based on multi-scale feature fusion for wood NIR classification

With the rapid development of deep learning techniques, convolutional neural networks have been widely used in the field of spectroscopy. In this paper, a bilinear branching Densenet network model (BO-Densenet) based on multi-scale feature fusion is constructed by applying a one-dimensional convolutional neural network to classify six woods: Tung wood, Balsa wood, Poplar wood, PVA-modified Poplar wood, Nano-silica-sol modified Poplar wood, and PVA-Nano-silica-sol modified Poplar wood. The results show that BO-Densenet achieves 98.90% accuracy in classification on the test set, which is higher than 82.09% of Partial Least Squares, and also higher than 89.88% of Lenet, 93.56% of Alexnet, 94.12% of Resnet-18 and 96.69% of Densenet-40 when compared with other deep learning algorithms. This shows that the BO-Densenet proposed in this paper can accurately achieve wood classification and has potential application prospects.