A Comparative Analysis of Oak Wood Defect Detection Using Two Deep Learning (DL)-Based Software

IF 4.3 3区材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

ACS Applied Electronic Materials Pub Date : 2024-04-15 DOI:10.3390/asi7020030

Branimir Jambreković, Filip Veselčić, I. Ištok, T. Sinković, Vjekoslav Živković, T. Sedlar

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引用次数: 0

Abstract

The world’s expanding population presents a challenge through its rising demand for wood products. This requirement contributes to increased production and, ultimately, the high-quality and efficient utilization of basic materials. Detecting defects in wood elements, which are inevitable when working with a natural material such as wood, is one of the difficulties associated with the issue above. Even in modern times, people still identify wood defects by visually scrutinizing the sawn surface and marking the defects. Industrial scanners equipped with software based on convolutional neural networks (CNNs) allow for the rapid detection of defects and have the potential to accelerate production and eradicate human subjectivity. This paper evaluates the suitability of defect recognition software in industrial scanners against software specifically designed for this task within a research project conducted using Adaptive Vision Studio, focusing on feature detection techniques. The research revealed that the software installed as part of the industrial scanner is more effective for analyzing knots (77.78% vs. 70.37%), sapwood (100% vs. 80%), and ambrosia wood (60% vs. 20%), while the software derived from the project is more effective for analyzing cracks (70% vs. 65%), ingrown bark (42.86% vs. 28.57%), and wood rays (81.82% vs. 27.27%).

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使用两种基于深度学习 (DL) 的软件检测橡木缺陷的对比分析

世界人口不断增长，对木制品的需求不断增加，这给我们带来了挑战。这种需求有助于提高产量，最终实现基本材料的高质量和高效率利用。在使用木材这种天然材料时，检测木质元素的缺陷是不可避免的，这也是与上述问题相关的困难之一。即使在现代，人们仍然通过目测锯切表面和标记缺陷来识别木材缺陷。配备了基于卷积神经网络（CNN）软件的工业扫描仪可以快速检测缺陷，并有可能加快生产速度和消除人的主观性。本文在一个使用 Adaptive Vision Studio 开展的研究项目中，评估了工业扫描仪中的缺陷识别软件与专门为此任务设计的软件的适用性，重点关注特征检测技术。研究表明，作为工业扫描仪一部分安装的软件在分析木结（77.78% 对 70.37%）、边材（100% 对 80%）和伏木（60% 对 20%）方面更有效，而从项目中衍生的软件在分析裂纹（70% 对 65%）、树皮内生（42.86% 对 28.57%）和木射线（81.82% 对 27.27%）方面更有效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Applied Electronic Materials Multiple-

CiteScore

7.20

自引率

4.30%

发文量

567

期刊介绍： ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric. Indexed/Abstracted： Web of Science SCIE Scopus CAS INSPEC Portico