Performance Influencing Factors of Convolutional Neural Network Models for Classifying Certain Softwood Species

Abstract

This study aims to verify the wood classification performance of convolutional neural networks (CNNs), such as VGG16, ResNet50, GoogLeNet, and basic CNN architectures, and to investigate the factors affecting classification performance. A dataset from 10 softwood species consisted of 200 cross-sectional micrographs each from the total part, earlywood, and latewood of each species. We used 80% and 20% of each dataset for training and testing, respectively. To improve the performance of the architectures, the dataset was augmented, and the differences in classification performance before and after augmentation were compared. The four architectures showed a high classification accuracy of over 90% between species, and the accuracy increased with increasing epochs. However, the starting points of the accuracy, loss, and training speed increments differed according to the architecture. The latewood dataset showed the highest accuracy. The epochs and augmented datasets also positively affected accuracy, whereas the total part and non-augmented datasets had a negative effect on accuracy. Additionally, the augmented dataset tended to derive stable results and reached a convergence point earlier. In the present study, an augmented latewood dataset was the most important factor affecting classification performance and should be used for training CNNs.

Convolutional neural network performance and the factors affecting performance for classification of seven Quercus species using sclereid characteristics in the bark

“…In contrast, the non-augmented dataset took approximately 40 to 50 epochs for the optimizer conditions to reach a stable state. The results obtained in this study are in line with previous results from the authors (Kim et al 2023), which demonstrated that the utilization of an augmented dataset resulted in a more rapid stabilization of loss and classification accuracy compared to the non−augmented dataset. Rapid stabilization can also be ascribed to the advantages of using data augmentation, which includes mitigating the occurrence of overfitting and enhancing the classification accuracy (Wong et al 2016;Fujita and Takahara 2017;Shorten and Khoshgoftaar 2019).…”