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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| Format: | Final Year Project / Dissertation / Thesis |
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2019
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| Online Access: | http://eprints.utar.edu.my/3630/ http://eprints.utar.edu.my/3630/1/ESA%2D2019%2D1601225%2D1.pdf |
| _version_ | 1848885950897192960 |
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| author | Tang, Xin Jie |
| author_facet | Tang, Xin Jie |
| author_sort | Tang, Xin Jie |
| building | UTAR Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T19:30:45Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-3630 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:30:45Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-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/ |
| spellingShingle | TA Engineering (General). Civil engineering (General) Tang, Xin Jie Design and Development of a Practical Macroscopic Wood Identification System Using Deep Learning |
| title | 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_short | 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 |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.utar.edu.my/3630/ http://eprints.utar.edu.my/3630/1/ESA%2D2019%2D1601225%2D1.pdf |