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Open AccessArticle

Wood Defect Detection Based on Depth Extreme Learning Machine

1
College of Mechanical & Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
2
School of Artificial Intelligence, Hezhou University, Hezhou 542899, China
3
Nanjing Fujitsu Nanda Software Technology Co., Ltd., Nanjing 210012, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(21), 7488; https://doi.org/10.3390/app10217488
Received: 18 September 2020 / Revised: 20 October 2020 / Accepted: 22 October 2020 / Published: 24 October 2020
(This article belongs to the Special Issue Mathematics and Digital Signal Processing)
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. View Full-Text
Keywords: wood defect; CNN; ELM; genetic algorithm; detection wood defect; CNN; ELM; genetic algorithm; detection
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Yang, Y.; Zhou, X.; Liu, Y.; Hu, Z.; Ding, F. Wood Defect Detection Based on Depth Extreme Learning Machine. Appl. Sci. 2020, 10, 7488.

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