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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.1
- 수록면
- 797 - 808 (12page)
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Automatic wood species identification systems have enabled fast and accurate identification of wood species outside of specialized laboratories with well-trained experts on wood species identification. Conventional automatic wood species identification systems consist of two major parts: a feature extractor and a classifier. Feature extractors require hand-engineering to obtain optimal features to quantify the content of an image. A Convolutional Neural Network (CNN), which is one of the Deep Learning methods, trained for wood species can extract intrinsic feature representations and classify them correctly. It usually outperforms classifiers built on top of extracted features with a hand-tuning process. We developed an automatic wood species identification system utilizing CNN models such as LeNet, MiniVGGNet, and their variants. A smartphone camera was used for obtaining macroscopic images of rough sawn surfaces from cross sections of woods. Five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch) were under classification by the CNN models. The highest and most stable CNN model was LeNet3 that is two additional layers added to the original LeNet architecture. The accuracy of species identification by LeNet3 architecture for the five Korean softwood species was 99.3%. The result showed the automatic wood species identification system is sufficiently fast and accurate as well as small to be deployed to a mobile device such as a smartphone.
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참고문헌 (35)
참고문헌 신청
Cavalin, P. R. / 2016 / An adaptive multi-level framework for forest species recognition / Proc. of 2015 Brazillian Conference on Intelligent Systems (BRAICS 2015) :92~97
Cavalin, P. R. / 2013 / A multiple feature vector framework for forest species recognition / Proc. of the ACM Symposium on Applied Computing :16~20
Hafemann, L. G. / 2014 / Forest Species Recognition Using Deep Convolutional Neural Networks / 22nd International Conference on Pattern Recognition (ICPR) :1103~1107
Haindl, M. / 2015 / Computer Analysis of Images and Patterns, Lecture Notes in Computer Science / Springer International Publishing :300~311
He, K. / 2016 / Deep Residual Learning for Image Recognition / 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) :770~778
Cavalin, P. R. / 2013 / A multiple feature vector framework for forest species recognition / Proc. of the ACM Symposium on Applied Computing :16~20
Hafemann, L. G. / 2014 / Forest Species Recognition Using Deep Convolutional Neural Networks / 22nd International Conference on Pattern Recognition (ICPR) :1103~1107
Haindl, M. / 2015 / Computer Analysis of Images and Patterns, Lecture Notes in Computer Science / Springer International Publishing :300~311
He, K. / 2016 / Deep Residual Learning for Image Recognition / 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) :770~778
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