A Novel Deep Convolutional Neural Network Based on ResNet-18 and Transfer Learning for Detection of Wood Knot Defects

Mingyu Gao,Fei Wang,Peng Song,Dawei Qi,Junyan Liu,Andreas Mandelis,Ruizhen Yang

doi:10.1155/2021/4428964

Mingyu Gao, Fei Wang + Show 5 more

Open Access

https://doi.org/10.1155/2021/4428964

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Abstract

Wood defects are quickly identified from an optical image based on deep learning methodology, which effectively improves wood utilization. Traditional neural network techniques have not yet been employed for wood defect detection due to long training time, low recognition accuracy, and nonautomatical extraction of defect image features. In this work, a model (so-called ReSENet-18) for wood knot defect detection that combined deep learning and transfer learning is proposed. The “squeeze-and-excitation” (SE) module is firstly embedded into the “residual basic block” structure for a “SE-Basic-Block” module construction. This model has the advantages of the features that are extracted in the channel dimension, and it is fused in multiscale with original features. Instantaneously, the fully connected layer is replaced with a global average pooling; consequently, the model parameters could be reduced effectively. The experimental results show that the accuracy has reached 99.02%, meanwhile the training time is also reduced. It shows that the proposed deep convolutional neural network based on ReSENet-18 combined with transfer learning can improve the accuracy of defect recognition and has a potential application in the detection of wood knot defects.

Highlights

Wood knot defect detection is an important part in the production of wood products and affects the quality of wood products
In the feature extraction part of the network, the SE module is embedded into the residual basic blocks to form SE-Basic-Block
The classifier of the network selects the global average pool to replace the fully connected layer after the convolutional layer at the end to speed up the convergence speed and reduce the model parameters. 2521 images of wood knot defects were used for training after 200 training epochs

Summary

Introduction

Wood knot defect detection is an important part in the production of wood products and affects the quality of wood products. Lin et al in 2015 proposed a method to classify wood knot defects by combining the aspect ratio, grayscale, and variance feature extraction method of the back propagation (BP) network [10] The accuracy of this method can reach 86.67%. Mu et al proposed a wood defect classification method by extracting the perimeter, area, aspect ratio, and mean grayscale value of the defect, combined with the radial basis function (RBF) neural network with the accuracy over 85% [11]. To solve these problems and improve the accuracy of the model, a high accuracy wood knot defect detection method based on the convolutional neural network is required. A model based on the attention mechanism and deep transfer residual convolutional neural network structure named ReSENet-18 is proposed to detect wood knot defects. Based on a benchmark dataset, the test results are compared and analyzed with other deep learning models

Image Processing and Methods

Experimental Results and Discussion

Convergence and Prediction Accuracy Analysis of ReSENet-18 Network Model

Conclusions