Comprehensive Evaluation of Deep Neural Network Architectures for Parawood Pith Estimation

被引：0

作者：

Kurdthongmee W. ^{[1
]}

机构：

[1] School of Engineering and Technology, Walailak University, 222 Thaiburi, Thasala, Nakhon Si Thammarat

来源：

HighTech and Innovation Journal | 2023年 / 4卷 / 03期

关键词：

Accuracy; Deep Learning; Image Augmentation; Mobilenet; Parawood; Regression; Resnet; Wood Pith Detection; Xception;

D O I：

10.28991/HIJ-2023-04-03-06

中图分类号：

学科分类号：

摘要：

Accurate pith estimation is crucial for maintaining the quality of wood products. This study delves into deep learning techniques for precise Parawood pith estimation, employing popular convolutional neural networks (ResNet50, MobileNet, and Xception) with adapted regression heads. Through variations in regression functions, optimizers, and training epochs, the most effective models were pinpointed. Xception, coupled with Huber Loss regression, Nadam optimizer, and 200 epochs, showcased superior performance, achieving a 4.48 mm mean error (with a standard deviation of 3.69 mm) in Parawood. Notably, benchmarking on the Douglas Fir dataset yielded similar results (2.81 mm mean error, standard deviation: 1.57 mm). These findings underscore deep learning's potential for Parawood and Douglas Fir pith estimation, offering substantial benefits to wood industry quality control and production efficiency. By harnessing advanced machine learning techniques, this study advances wood industry processes, promoting the adoption of state-of-the-art technology in forestry and wood science. © 2023, Ital Publication. All rights reserved.

引用

页码：543 / 559

页数：16

共 22 条

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