Wood Defect Detection Based on Depth Extreme Learning Machine

Yutu Yang,Fenglong Ding,Ying Liu,Xiaolin Zhou,Zhongkang Hu

doi:10.3390/app10217488

Abstract

The deep learning feature extraction method and extreme learning machine (ELM) classification method are combined to establish a depth extreme learning machine model for wood image defect detection. The convolution neural network (CNN) algorithm alone tends to provide inaccurate defect locations, incomplete defect contour and boundary information, and inaccurate recognition of defect types. The nonsubsampled shearlet transform (NSST) is used here to preprocess the wood images, which reduces the complexity and computation of the image processing. CNN is then applied to manage the deep algorithm design of the wood images. The simple linear iterative clustering algorithm is used to improve the initial model; the obtained image features are used as ELM classification inputs. ELM has faster training speed and stronger generalization ability than other similar neural networks, but the random selection of input weights and thresholds degrades the classification accuracy. A genetic algorithm is used here to optimize the initial parameters of the ELM to stabilize the network classification performance. The depth extreme learning machine can extract high-level abstract information from the data, does not require iterative adjustment of the network weights, has high calculation efficiency, and allows CNN to effectively extract the wood defect contour. The distributed input data feature is automatically expressed in layer form by deep learning pre-training. The wood defect recognition accuracy reached 96.72% in a test time of only 187 ms.

Highlights

Today’s wood products are manufactured under increasingly stringent requirements for surface processing
The depth extreme learning machine can extract high-level abstract information from the data, does not require iterative adjustment of the network weights, has high calculation efficiency, and allows convolution neural network (CNN) to effectively extract the wood defect contour
This paper proposes an optimized algorithm which uses nonsubsampled shearlet transform (NSST) to preprocess the images This paper optimized which usesimages to the CNN

Summary

Introduction

Today’s wood products are manufactured under increasingly stringent requirements for surface processing. In developed countries such as Sweden and Finland with developed forest resources, the comprehensive use rate of wood is as high as 90%. With China’s rapid economic development, people are increasingly pursuing a high-quality life, which will inevitably lead to an increase in demand for wood and wood products, such as solid wood panels, wood-based panels, paper and cardboard, and other consumption levels are among the highest in the world. The existing wood storage capacity and processing level make it difficult to meet the rapid growth demand. The lack of wood supply and the low use rate have led to the limited development of China’s wood industry

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Conclusion