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http://dx.doi.org/10.5658/WOOD.2019.47.2.229

Visual Classification of Wood Knots Using k-Nearest Neighbor and Convolutional Neural Network  

Kim, Hyunbin (Department of Forest Sciences, Seoul National University)
Kim, Mingyu (Department of Forest Sciences, Seoul National University)
Park, Yonggun (Department of Forest Sciences, Seoul National University)
Yang, Sang-Yun (Department of Forest Sciences, Seoul National University)
Chung, Hyunwoo (Department of Forest Sciences, Seoul National University)
Kwon, Ohkyung (National Instrumentation Center for Environmental Management, Seoul National University)
Yeo, Hwanmyeong (Department of Forest Sciences, Seoul National University)
Publication Information
Journal of the Korean Wood Science and Technology / v.47, no.2, 2019 , pp. 229-238 More about this Journal
Abstract
Various wood defects occur during tree growing or wood processing. Thus, to use wood practically, it is necessary to objectively assess their quality based on the usage requirement by accurately classifying their defects. However, manual visual grading and species classification may result in differences due to subjective decisions; therefore, computer-vision-based image analysis is required for the objective evaluation of wood quality and the speeding up of wood production. In this study, the SIFT+k-NN and CNN models were used to implement a model that automatically classifies knots and analyze its accuracy. Toward this end, a total of 1,172 knot images in various shapes from five domestic conifers were used for learning and validation. For the SIFT+k-NN model, SIFT technology was used to extract properties from the knot images and k-NN was used for the classification, resulting in the classification with an accuracy of up to 60.53% when k-index was 17. The CNN model comprised 8 convolution layers and 3 hidden layers, and its maximum accuracy was 88.09% after 1205 epoch, which was higher than that of the SIFT+k-NN model. Moreover, if there is a large difference in the number of images by knot types, the SIFT+k-NN tended to show a learning biased toward the knot type with a higher number of images, whereas the CNN model did not show a drastic bias regardless of the difference in the number of images. Therefore, the CNN model showed better performance in knot classification. It is determined that the wood knot classification by the CNN model will show a sufficient accuracy in its practical applicability.
Keywords
visual classification; knot classification; k-nearest neighbor; convolution neural network; deep learning; species identification; wood classification;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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1 Eom, Y., Park, B. 2018. Wood Species Identification of Documentary Woodblocks of Songok Clan of the Milseong Park, Gyeongju, Korea. Journal of the Korean Wood Science and Technology 46(3): 270-277.   DOI
2 Hu, S., Li, K., Bao, X. 2015. Wood species recognition based on SIFT keypoint histogram, Image and Signal Processing (CISP), 2015 8th International Congress on IEEE, pp. 702-706.
3 Kim, S.C., Choi, J. 2016. Study on Wood Species Identification for Daeungjeon Hall of Jeonghyesa Temple, Suncheon. Journal of the Korean Wood Science and Technology 44(6): 897-902.   DOI
4 LeCun, Y., Bengio, Y., Hinton, G. 2015. Deep learning. Nature 521, p. 436.   DOI
5 Kwon, O., Lee, H.G., Lee, M.R., Jang, S., Yang, S.Y., Park, S.Y., Choi, I., Yeo, H. 2017. Automatic Wood Species Identification of Korean Softwood Based on Convolutional Neural Networks. Journal of the Korean Wood Science and Technology 45(6): 797-808.   DOI
6 KS F 2151. 2014, Visual grading for softwood structural lumber. Korean Agency for Technology and Standard.
7 Lampinen, J., Smolander, S., Korhonen, M. 1998. Wood surface inspection system based on generic visual features. Industrial Applications of Neural Networks, pp. 35-42.
8 Lee, K., Seo, J., Han, G. 2018. Dating Wooden Artifacts Excavated at Imdang-dong Site, Gyeongsan, Korea and Interpreting the Paleoenvironment according to the Wood Identification. Journal of the Korean Wood Science and Technology 46(3): 241-252.   DOI
9 Mohan, S., Venkatachalapathy, K. 2012. Wood knot classification using bagging. International Journal of Computer Applications 51(18).
10 Norlander, R., Grahn, J., Maki, A. 2015. Wooden knot detection using convnet transfer learning. In Scandinavian Conference on Image Analysis, pp. 263-274.
11 Park, J.H., Oh, J.E., Hwang, I.S., Jang, H.U., Choi, J.W., Kim, S.C. 2018. Study on Species Identification for Pungnammun Gate (Treasure 308) in Jeonju, Korea. Journal of the Korean Wood Science and Technology 46(3): 278-284.   DOI
12 Tong, H.L., Ng, H., Yap, T.V.T., Ahmad, W.S.H.M.W., Fauzi, M.F.A. 2017. Evaluation of feature extraction and selection techniques for the classification of wood defect images. Journal of Engineering and Applied Science 12(3): 602-608.
13 Park, S.Y., Kim, J.C., Kim, J.H., Yang, S.Y., Kwon, O., Yeo, H., Cho, K., Choi, I.G. 2017. Possibility of Wood Classification in Korean Softwood Species Using Near-infrared Spectroscopy Based on Their Chemical Compositions. Journal of the Korean Wood Science and Technology 45(2): 202-212.   DOI
14 Putri, D.I.H., Machbub, C. 2018. Object detection and tracking using SIFT-KNN classifier and Yaw-Pitch servo motor control on humanoid robot. In Signals and Systems (ICSigSys), 2018 International Conference on IEEE, pp. 47-52.
15 Thomas, E. 2017. An artificial neural network for real-time hardwood lumber grading. Computers and Electronics in Agriculture 132: 71-75.   DOI