Abstract
With the wide increase in global forestry resources trade, the demand for wood is increasing day by day, especially rare wood. Finding a computer-based method that can identify wood species has strong practical value and very important significance for regulating the wood trade market and protecting the interests of all parties, which is one of the important problems to be solved by the wood industry. This article firstly studies the establishment of wood microscopic images dataset through a combination of traditional image amplification technology and Mix-up technology expansion strategy. Then with the traditional Faster Region-based Convolutional Neural Networks (Faster RCNN) model, the receptive field enhancement Spatial Pyramid Pooling (SPP) module and the multi-scale feature fusion of Feature Pyramid Networks (FPN) module are introduced to construct a microscopic image identification model based on the migration learning fusion model and analyzes the three factors (Mix-up, Enhanced SPP and FPN modules) affecting the wood microscopic image detection model. The experimental results show that the proposed approach can identify 10 kinds of wood microscopic images, and the accuracy rate has increased from 77.8% to 83.8%, which provides convenient conditions for further in-depth study of the microscopic characteristics of wood cells and is of great significance to the field of wood science.
Highlights
Wood is the basic raw material of wood products and the basic form of wood industry products
Lopes et al [12] used a commercial smartphone equipped with a 14x macro camera lens to capture texture images of 10 North American hardwoods, and constructed a dataset of 1869 images, which identified hardwood species from the macro images using InceptionV4_ResNetV2 convolutional neural network (CNN)
Table 1. shows the detection accuracy of each category after the standard Faster RCNN algorithm is trained on different wood microscopic image datasets
Summary
Wood is the basic raw material of wood products and the basic form of wood industry products. Lopes et al [12] used a commercial smartphone equipped with a 14x macro camera lens to capture texture images of 10 North American hardwoods, and constructed a dataset of 1869 images, which identified hardwood species from the macro images using InceptionV4_ResNetV2 convolutional neural network (CNN) He et al [13] obtained 10,237 images from the cross-sections of 417 wood specimens of 15 Dalbergia and 11 Pterocarpus species. Lopes et al [12] presented the feasibility of the InceptionV4_ResNetV2 convolutional neural network to classify 10 North American hardwood species with 92.60% of accuracy and precision–recall rate of 0.98 He et al [13] developed three deep learning models to distinguish wood types, which analyzed the optimal parameters of the deep learning model and visualized the representative wood anatomical features activated by the deep learning model. The images of each tree species come from multiple sections, and the wood microscopic image sample library and dataset is established
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