A review of recent deep learning applications in wood surface defect identification

Hashim, Ummi Rabaah and Ali, Martina and Kanchymalay, Kasturi and Wibawa, Aji Prasetya and Salahuddin, Lizawati and Rahiddin, Rahillda Nadhirah Norizzaty Rahiddin (2025) A review of recent deep learning applications in wood surface defect identification. IAES International Journal of Artificial Intelligence (IJ-AI), 14 (3). pp. 1696-1707. ISSN 2089-4872

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Abstract

Wood is widely used in construction, art, and home applications due to its aesthetic appeal and favorable mechanical properties. However, environmental factors significantly affect the growth and preservation of wood, often leading to defects that can reduce its performance and ornamental value. Researchers have introduced machine vision and deep learning methods to address the challenges of high labor costs and inefficiencies in identifying wood defects. Deep learning has shown great success in image recognition tasks, yielding impressive results. This paper reviews previous work on deep-learning strategies for identifying wood surface defects. It also discusses data augmentation techniques to address limited defect data and explores transfer learning to enhance classification accuracy on small datasets. Finally, the paper examines the potential limitations of deep learning for defect identification and suggests future research directions.

Item Type:	Article
Uncontrolled Keywords:	Automated inspection, Deep learning, Defect identification, Transfer learning, Wood surface defects
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	12 Dec 2025 01:52
Last Modified:	12 Dec 2025 01:52
URI:	http://eprints.utem.edu.my/id/eprint/29233
Statistic Details:	View Download Statistic

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