单板
卷积神经网络
人工智能
模式识别(心理学)
计算机科学
学习迁移
人工神经网络
鉴定(生物学)
钥匙(锁)
分类器(UML)
材料科学
复合材料
植物
生物
计算机安全
作者
Kai Hu,Baojin Wang,Yi Shen,Jieru Guan,Yi Cai
出处
期刊:Bioresources
[BioResources]
日期:2020-03-16
卷期号:15 (2): 3041-3052
被引量:18
标识
DOI:10.15376/biores.15.2.3041-3052
摘要
As the main production unit of plywood, the surface defects of veneer seriously affect the quality and grade of plywood. Therefore, a new method for identifying wood defects based on progressive growing generative adversarial network (PGGAN) and the MASK R-CNN model is presented. Poplar veneer was mainly studied in this paper, and its dead knots, live knots, and insect holes were identified and classified. The PGGAN model was used to expand the dataset of wood defect images. A key ideal employed the transfer learning in the base of MASK R-CNN with a classifier layer. Lastly, the trained model was used to identify and classify the veneer defects compared with the back- propagation (BP) neural network, self-organizing map (SOM) neural network, and convolutional neural network (CNN). Experimental results showed that under the same conditions, the algorithm proposed in this paper based on PGGAN and MASK R-CNN and the model obtained through the transfer learning strategy accurately identified the defects of live knots, dead knots, and insect holes. The accuracy of identification was 99.05%, 97.05%, and 99.10%, respectively.
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