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Design and Development of a Practical Macroscopic Wood Identification System Using Deep Learning

Wood serves as raw material for countless industries due to its unique material characteristics. As such, different wood types are graded and valued accordingly based on their commercial value as raw material. Hence, wood identification is needed to ensure the correct wood type for usage. Macrosco...

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Main Author: Tang, Xin Jie
Format: Final Year Project / Dissertation / Thesis
Published: 2019
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utar.edu.my/3630/1/ESA%2D2019%2D1601225%2D1.pdf
http://eprints.utar.edu.my/3630/
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spelling my-utar-eprints.36302019-12-17T09:13:40Z Design and Development of a Practical Macroscopic Wood Identification System Using Deep Learning Tang, Xin Jie TA Engineering (General). Civil engineering (General) Wood serves as raw material for countless industries due to its unique material characteristics. As such, different wood types are graded and valued accordingly based on their commercial value as raw material. Hence, wood identification is needed to ensure the correct wood type for usage. Macroscopic level wood identification that has been practiced by wood anatomists for decades can identify wood up to genus level for any commercial timber group. However, this knowledge is difficult to transfer to the industry non-experts. In this research, a rapid and robust macroscopic wood identification system is proposed using deep learning method with off-the-shelf smart-phone and retrofitted macro-lens as image acquisition device. Trained deep learning model is deployed as a cloud service accessible via Internet. This research collects and verifies data by wood anatomists on 100 Malaysian Tropical Timber types using the image acquisition device. A new Convolution Neural Network BlazeNet designed by the author, achieved better accuracy when benchmarked against SqueezeNet in this research. A cloud based wood identification system was deployed accompanied by an iOS application, Mywood-ID. 2019 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/3630/1/ESA%2D2019%2D1601225%2D1.pdf Tang, Xin Jie (2019) Design and Development of a Practical Macroscopic Wood Identification System Using Deep Learning. Master dissertation/thesis, UTAR. http://eprints.utar.edu.my/3630/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Tang, Xin Jie
Design and Development of a Practical Macroscopic Wood Identification System Using Deep Learning
description Wood serves as raw material for countless industries due to its unique material characteristics. As such, different wood types are graded and valued accordingly based on their commercial value as raw material. Hence, wood identification is needed to ensure the correct wood type for usage. Macroscopic level wood identification that has been practiced by wood anatomists for decades can identify wood up to genus level for any commercial timber group. However, this knowledge is difficult to transfer to the industry non-experts. In this research, a rapid and robust macroscopic wood identification system is proposed using deep learning method with off-the-shelf smart-phone and retrofitted macro-lens as image acquisition device. Trained deep learning model is deployed as a cloud service accessible via Internet. This research collects and verifies data by wood anatomists on 100 Malaysian Tropical Timber types using the image acquisition device. A new Convolution Neural Network BlazeNet designed by the author, achieved better accuracy when benchmarked against SqueezeNet in this research. A cloud based wood identification system was deployed accompanied by an iOS application, Mywood-ID.
format Final Year Project / Dissertation / Thesis
author Tang, Xin Jie
author_facet Tang, Xin Jie
author_sort Tang, Xin Jie
title Design and Development of a Practical Macroscopic Wood Identification System Using Deep Learning
title_short Design and Development of a Practical Macroscopic Wood Identification System Using Deep Learning
title_full Design and Development of a Practical Macroscopic Wood Identification System Using Deep Learning
title_fullStr Design and Development of a Practical Macroscopic Wood Identification System Using Deep Learning
title_full_unstemmed Design and Development of a Practical Macroscopic Wood Identification System Using Deep Learning
title_sort design and development of a practical macroscopic wood identification system using deep learning
publishDate 2019
url http://eprints.utar.edu.my/3630/1/ESA%2D2019%2D1601225%2D1.pdf
http://eprints.utar.edu.my/3630/
_version_ 1657491981613400064
score 13.211869

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