Open AccessDefect identification method for poplar veneer based on progressive growing generated adversarial network and MASK R-CNN model
Author(s) -
Kai Hu,
Baojin Wang,
Yi Shen,
Jieru Guan,
Yi Cai
Publication year - 2020
Publication title -
bioresources
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 69
ISSN - 1930-2126
DOI - 10.15376/biores.15.2.3041-3052
Subject(s) - veneer , convolutional neural network , artificial intelligence , pattern recognition (psychology) , computer science , transfer of learning , artificial neural network , identification (biology) , key (lock) , classifier (uml) , materials science , composite material , botany , biology , computer security
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.