지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.1
- 수록면
- 265 - 276 (12page)
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초록· 키워드
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In our previous study, the LeNet3 model successfully classified images from the transverse surfaces of five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch). However, a practical limitation exists in our system stemming from the nature of the training images obtained from the transverse plane of the wood species. In real-world applications, it is necessary to utilize images from the longitudinal surfaces of lumber. Thus, we improved our model by training it with images from the longitudinal and transverse surfaces of lumber. Because the longitudinal surface has complex but less distinguishable features than the transverse surface, the classification performance of the LeNet3 model decreases when we include images from the longitudinal surfaces of the five Korean softwood species. To remedy this situation, we adopt ensemble methods that can enhance the classification performance. Herein, we investigated the use of ensemble models from the LeNet and MiniVGGNet models to automatically classify the transverse and longitudinal surfaces of the five Korean softwoods. Experimentally, the best classification performance was achieved via an ensemble model comprising the LeNet2, LeNet3, and MiniVGGNet4 models trained using input images of $128{\times}128{\times}3pixels$ via the averaging method. The ensemble model showed an F1 score greater than 0.98. The classification performance for the longitudinal surfaces of Korean pine and Korean red pine was significantly improved by the ensemble model compared to individual convolutional neural network models such as LeNet3.
목차
등록된 정보가 없습니다.
참고문헌 (18)
참고문헌 신청
엄유정 / 2018 / 밀성박씨 경주 손곡문중 목판의 수종식별 / 목재공학 46(3):270~277
Hafemann, L. G. / 2014 / Forest Species Recognition Using Deep Convolutional Neural Networks / 22nd International Conference on Pattern Recognition(ICPR) :1103~1107
Hermanson, J. C. / 2011 / A brief review of machine vision in the context of automated wood identification systems / IAWA Journal 32(2):233~250
Hermanson, J. / 2013 / A machine vision system for automated field-level wood identification. Global Timber Tracking Network / Presentation at Regional Workshop for Asia, Pacific and Oceania on identification of timber species and origins
Khalid, M. / 2008 / Design of an intelligent wood species recognition system. International Journal of Simulation System / Science and Technology 9(3):9~19
Hafemann, L. G. / 2014 / Forest Species Recognition Using Deep Convolutional Neural Networks / 22nd International Conference on Pattern Recognition(ICPR) :1103~1107
Hermanson, J. C. / 2011 / A brief review of machine vision in the context of automated wood identification systems / IAWA Journal 32(2):233~250
Hermanson, J. / 2013 / A machine vision system for automated field-level wood identification. Global Timber Tracking Network / Presentation at Regional Workshop for Asia, Pacific and Oceania on identification of timber species and origins
Khalid, M. / 2008 / Design of an intelligent wood species recognition system. International Journal of Simulation System / Science and Technology 9(3):9~19
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