Wood Classification of Japanese Fagaceae using Partial Sample Area and Convolutional Neural Networks = Wood Classification of Japanese Fagaceae using Partial Sample Area and Convolutional Neural Networks

다국어 초록 (Multilingual Abstract)

Wood identification is regularly performed by observing the wood anatomy, such as colour, texture, fibre direction, and other characteristics. The manual process, however, could be time consuming, especially when identification work is required at high quantity. Considering this condition, a convolutional neural networks (CNN)-based program is applied to improve the image classification results. The research focuses on the algorithm accuracy and efficiency in dealing with the dataset limitations. For this, it is proposed to do the sample selection process or only take a small portion of the existing image. Still, it can be expected to represent the overall picture to maintain and improve the generalisation capabilities of the CNN method in the classification stages. The experiments yielded an incredible F1 score average up to 93.4% for medium sample area sizes (200 × 200 pixels) on each CNN architecture (VGG16, ResNet50, MobileNet, DenseNet121, and Xception based). Whereas DenseNet121-based architecture was found to be the best architecture in maintaining the generalisation of its model for each sample area size (100, 200, and 300 pixels). The experimental results showed that the proposed algorithm can be an accurate and reliable solution.

참고문헌 (Reference)

1 전우석, "국내산 대나무 3종의 해부학적 특성" 한국목재공학회 46 (46): 29-37, 2018

2 Chollet, F., "Xception: Deep Learning with Depthwise Separable Convolutions" 1251-1258, 2017

3 Prislan, P., "Wood sample preparation for microscopic analysis" University of Ljubljana, Department of Wood Science and Technology 2014

4 Sugiarto, B., "Wood identification based on histogram of oriented gradient (HOG) feature and support vector machine(SVM) classifier" 337-341, 2017

5 양상윤, "Wood Species Classification Utilizing Ensembles of Convolutional Neural Networks Established by Near-Infrared Spectra and Images Acquired from Korean Softwood Lumber" 한국목재공학회 47 (47): 385-392, 2019

6 Salma, S., "Wood Identification on Microscopic Image with Daubechies Wavelet Method and Local Binary Pattern" 23-27, 2018

7 황성욱, "Wood Identification of Historical Architecture in Korea by Synchrotron X-ray Microtomography-Based Three-Dimensional Microstructural Imaging" 한국목재공학회 48 (48): 283-290, 2020

8 Schoch, W., "Wood Anatomy of Central European Species" Swiss Federal Institute for Forest 2004

9 Simonyan, K., "Very deep convolutional networks for large-scale image recognition"

10 Sewak, M., "Practical Convolutional Neural Networks: Implement Advanced Deep Learning Models Using Python" Packt Publishing Ltd 2018

11 권오경, "Performance Enhancement of Automatic Wood Classification of Korean Softwood by Ensembles of Convolutional Neural Networks" 한국목재공학회 47 (47): 265-276, 2019

12 Fukushima, K., "Neocognitron : A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position" 36 : 193-202, 1980

13 Howard, A. G., "Mobilenets : Efficient convolutional neural networks for mobile vision applications"

14 Alvin Muhammad SAVERO, "Investigating the Anatomical and Physical-Mechanical Properties of the 8-Year-Old Superior Teakwood Planted in Muna Island, Indonesia" 한국목재공학회 48 (48): 618-630, 2020

15 Géron, A, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" O’Reilly Media 2019

16 Hadiwidjaja, M. L., "Developing wood identification system by local binary pattern and hough transform method" 1192 (1192): 012053-, 2019

17 Huang, G., "Densely Connected Convolutional Networks" 4700-4708, 2017

18 Marmanis, D., "Deep learning earth observation classification using ImageNet pretrained networks" 13 (13): 105-109, 2015

19 He, K., "Deep Residual Learning for Image Recognition" 770-778, 2016

20 Seth, W., "Deep Learning from Scratch" O’Reilly Media 2019

21 Maggiori, E., "Convolutional neural networks for large-scale remote-sensing image classification" 55 (55): 645-657, 2016

22 Yu, S., "Convolutional neural networks for hyperspectral image classification" 219 : 88-98, 2017

23 전우석, "Comparison of Anatomical Characteristics for Wood Damaged by Oak Wilt and Sound Wood from Quercus mongolica" 한국목재공학회 48 (48): 807-819, 2020

24 권오경, "Automatic Wood Species Identification of Korean Softwood Based on Convolutional Neural Networks" 한국목재공학회 45 (45): 797-808, 2017

25 Kobayashi, K., "Anatomical features of Fagaceae wood statistically extracted by computer vision approaches:Some relationships with evolution" 14 (14): e0220762-, 2019

26 Levi, G., "Age and Gender Classification Using Convolutional Neural Networks" 34-42, 2015

27 Hussain, M., "Advances in Computational Intelligence Systems" 191-202, 2018