期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：5
DOI：10.14569/IJACSA.2022.0130514
语种：English
出版社：Science and Information Society (SAI)
摘要：This paper discusses the efficacy of the data augmentation method deployed in many Convolutional Neural Network (CNN) algorithms for determining timber defect in four timber species from Malaysia. A sequence of morphological transformation, involving x-reflection and rotation, was executed in the timber defect augmentation dataset for aiding CNN model training and generating the finest CNN models which offer the best classification performance in determining timber defect. For further assessing the CNN algorithms’ classification performance, several deep learning hyperparameters were tried on the Merbau timber species by utilising epoch as well as learning rate. A comparison of the classification performance was then done between other timber classes, namely KSK, Meranti, and Rubberwood. According to the results, the ResNet50 algorithm, which has its basis in the transfer learning methodology, outclasses other CNN algorithms (ShuffleNet, AlexNet, MobileNetV2, NASNetMobile, and GoogLeNet) with the best classification accuracy of 94.59% using the data augmentation method. Furthermore, the outcomes indicate that utilising an augmentation methodology not just addresses the issue of a limited dataset but also enhances CNN classification output by 5.78% with the support of T-test that demonstrates a significant difference across all CNN algorithms except for Alexnet. Our study on hyperparameter optimisation by utilising learning rate as well as epoch is sufficient to infer that a greater number of epoch and learning rate does not deliver superior precision in CNN classification. The experimental findings suggest that the proposed methods improved CNN algorithms classification performance in identification of timber defect while tackling the imbalanced and limited dataset challenges.
关键词：Convolutional neural network; deep learning; defect identification; image augmentation; transfer learning
