Automatic Classification of Crack Severity from Cross-Section Image of Timber Using Simple Convolutional Neural Network
Cedar and other timbers used for construction generally undergo a high-temperature drying process after being harvested to maintain their quality. However, internal cracks occur during this process. This is an issue because it deteriorates the structural performance, such as buckling strength and jo...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/16/8250 |
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| author | Shigeru Kato Naoki Wada Kazuki Shiogai Takashi Tamaki Tomomichi Kagawa Renon Toyosaki Hajime Nobuhara |
| author_facet | Shigeru Kato Naoki Wada Kazuki Shiogai Takashi Tamaki Tomomichi Kagawa Renon Toyosaki Hajime Nobuhara |
| author_sort | Shigeru Kato |
| collection | DOAJ |
| description | Cedar and other timbers used for construction generally undergo a high-temperature drying process after being harvested to maintain their quality. However, internal cracks occur during this process. This is an issue because it deteriorates the structural performance, such as buckling strength and joint durability of the timber. Since preventing these internal cracks is difficult, their severity must be examined manually. Currently, the length, thickness, and area of the cracks on a cross-sectional surface of square timber are measured using calipers. However, this process is time-consuming and labor-intensive. Therefore, we employed a convolutional neural network (CNN), widely used in artificial intelligence applications, to automatically evaluate the severity of cracks from cross-sectional images of timber. A novel CNN was constructed and experimentally evaluated in this study. The average classification accuracy was 85.67%. |
| first_indexed | 2024-03-09T10:01:25Z |
| format | Article |
| id | doaj.art-523ba5a38a564595960070ce1ef146be |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T10:01:25Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-523ba5a38a564595960070ce1ef146be2023-12-01T23:22:06ZengMDPI AGApplied Sciences2076-34172022-08-011216825010.3390/app12168250Automatic Classification of Crack Severity from Cross-Section Image of Timber Using Simple Convolutional Neural NetworkShigeru Kato0Naoki Wada1Kazuki Shiogai2Takashi Tamaki3Tomomichi Kagawa4Renon Toyosaki5Hajime Nobuhara6Department of Electrical Engineering and Information Science, Niihama College, National Institute of Technology, 7-1 Yagumo-cho, Niihama 792-8580, JapanDepartment of Electrical Engineering and Information Science, Niihama College, National Institute of Technology, 7-1 Yagumo-cho, Niihama 792-8580, JapanDepartment of Electrical Engineering and Information Science, Niihama College, National Institute of Technology, 7-1 Yagumo-cho, Niihama 792-8580, JapanEhime Prefectural Forestry Research Center, 2-280-38 Sugou, Kumakougen-cho 792-1205, JapanDepartment of Electrical Engineering and Information Science, Niihama College, National Institute of Technology, 7-1 Yagumo-cho, Niihama 792-8580, JapanDepartment of Electrical Engineering and Information Science, Niihama College, National Institute of Technology, 7-1 Yagumo-cho, Niihama 792-8580, JapanFaculty of Engineering, Science and Information Systems, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba 305-8573, JapanCedar and other timbers used for construction generally undergo a high-temperature drying process after being harvested to maintain their quality. However, internal cracks occur during this process. This is an issue because it deteriorates the structural performance, such as buckling strength and joint durability of the timber. Since preventing these internal cracks is difficult, their severity must be examined manually. Currently, the length, thickness, and area of the cracks on a cross-sectional surface of square timber are measured using calipers. However, this process is time-consuming and labor-intensive. Therefore, we employed a convolutional neural network (CNN), widely used in artificial intelligence applications, to automatically evaluate the severity of cracks from cross-sectional images of timber. A novel CNN was constructed and experimentally evaluated in this study. The average classification accuracy was 85.67%.https://www.mdpi.com/2076-3417/12/16/8250timbercracksimple convolutional neural networkhuman fuzzinessmachine learning |
| spellingShingle | Shigeru Kato Naoki Wada Kazuki Shiogai Takashi Tamaki Tomomichi Kagawa Renon Toyosaki Hajime Nobuhara Automatic Classification of Crack Severity from Cross-Section Image of Timber Using Simple Convolutional Neural Network Applied Sciences timber crack simple convolutional neural network human fuzziness machine learning |
| title | Automatic Classification of Crack Severity from Cross-Section Image of Timber Using Simple Convolutional Neural Network |
| title_full | Automatic Classification of Crack Severity from Cross-Section Image of Timber Using Simple Convolutional Neural Network |
| title_fullStr | Automatic Classification of Crack Severity from Cross-Section Image of Timber Using Simple Convolutional Neural Network |
| title_full_unstemmed | Automatic Classification of Crack Severity from Cross-Section Image of Timber Using Simple Convolutional Neural Network |
| title_short | Automatic Classification of Crack Severity from Cross-Section Image of Timber Using Simple Convolutional Neural Network |
| title_sort | automatic classification of crack severity from cross section image of timber using simple convolutional neural network |
| topic | timber crack simple convolutional neural network human fuzziness machine learning |
| url | https://www.mdpi.com/2076-3417/12/16/8250 |
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