Timber defect identification: Enhanced classification with residual networks

This study investigates the potential enhancement of classification accuracy in timber defect identification through the utilization of deep learning, specifically residual networks. By exploring the refinement of these networks via increased depth and multi-level feature incorporation, the goal is...

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Bibliographic Details
Main Authors: Teo, Hong Chun, Hashim, Ummi Rabaah, Ahmad, Sabrina
Format: Article
Language:English
Published: Science and Information Organization 2024
Online Access:http://eprints.utem.edu.my/id/eprint/27549/2/0167809052024165055.PDF
http://eprints.utem.edu.my/id/eprint/27549/
https://thesai.org/Downloads/Volume15No4/Paper_68-Timber_Defect_Identification.pdf
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