Classification of defects in wooden structures using pre-trained models of convolutional neural network
<p>Wooden structures, over time, are challenged by different types of defects. Due to mechanical and weathering effects, these defects can occur in the form of cracks, live and dead knots, dampness, and others. Because of the risk of damage or complete failure, treatment of these defects is ne...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2023
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| _version_ | 1864513529500925952 |
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| author | Rana Ehtisham (17820980) |
| author2 | Waqas Qayyum (17820983) Charles V. Camp (17820986) Vagelis Plevris (14158863) Junaid Mir (17820989) Qaiser-uz Zaman Khan (17820992) Afaq Ahmad (5153747) |
| author2_role | author author author author author author |
| author_facet | Rana Ehtisham (17820980) Waqas Qayyum (17820983) Charles V. Camp (17820986) Vagelis Plevris (14158863) Junaid Mir (17820989) Qaiser-uz Zaman Khan (17820992) Afaq Ahmad (5153747) |
| author_role | author |
| dc.creator.none.fl_str_mv | Rana Ehtisham (17820980) Waqas Qayyum (17820983) Charles V. Camp (17820986) Vagelis Plevris (14158863) Junaid Mir (17820989) Qaiser-uz Zaman Khan (17820992) Afaq Ahmad (5153747) |
| dc.date.none.fl_str_mv | 2023-12-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.cscm.2023.e02530 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Classification_of_defects_in_wooden_structures_using_pre-trained_models_of_convolutional_neural_network/25036391 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Civil engineering Materials engineering Information and computing sciences Computer vision and multimedia computation Machine learning Wooden defects Defects Classification Pre-trained Models CNN |
| dc.title.none.fl_str_mv | Classification of defects in wooden structures using pre-trained models of convolutional neural network |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Wooden structures, over time, are challenged by different types of defects. Due to mechanical and weathering effects, these defects can occur in the form of cracks, live and dead knots, dampness, and others. Because of the risk of damage or complete failure, treatment of these defects is necessary, but doing so necessitates their proper identification and classification (categorization). Crack identification and categorization must be part of the inspection procedure for engineering structures in the built environment. Convolutional neural networks (CNNs), a sub-type of Deep Learning (DL), can automatically classify the images of wooden structures to identify such defects. In this study, ten pre-trained models of CNN, namely ResNet18, ResNet50, ResNet101, ShuffleNet, GoogLeNet, Inception-V3, MobileNet-V2, Xception, Inception-ResNet-V2, and NASNet-Mobile are evaluated for the tasks of classification and prediction of defects in wooden structures. Each pre-trained CNN model is additionally trained and validated on an image dataset of 9000 images, equally divided into three classes: cracks, knots, and intact (undamaged). A smaller dataset of 300 images is separately used for testing purposes. Statistical parameters such as accuracy, precision, recall, and F1-score are computed for each CNN model. The Inception-V3 model proved to be the best CNN model for classifying defects in wooden structures based on the model’s accuracy, processing time and overall performance.</p><h2>Other Information</h2> <p> Published in: Case Studies in Construction Materials<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.cscm.2023.e02530" target="_blank">https://dx.doi.org/10.1016/j.cscm.2023.e02530</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_289e0dca1d6b5fde6a0a6c4738615356 |
| identifier_str_mv | 10.1016/j.cscm.2023.e02530 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25036391 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Classification of defects in wooden structures using pre-trained models of convolutional neural networkRana Ehtisham (17820980)Waqas Qayyum (17820983)Charles V. Camp (17820986)Vagelis Plevris (14158863)Junaid Mir (17820989)Qaiser-uz Zaman Khan (17820992)Afaq Ahmad (5153747)EngineeringCivil engineeringMaterials engineeringInformation and computing sciencesComputer vision and multimedia computationMachine learningWooden defectsDefects ClassificationPre-trained ModelsCNN<p>Wooden structures, over time, are challenged by different types of defects. Due to mechanical and weathering effects, these defects can occur in the form of cracks, live and dead knots, dampness, and others. Because of the risk of damage or complete failure, treatment of these defects is necessary, but doing so necessitates their proper identification and classification (categorization). Crack identification and categorization must be part of the inspection procedure for engineering structures in the built environment. Convolutional neural networks (CNNs), a sub-type of Deep Learning (DL), can automatically classify the images of wooden structures to identify such defects. In this study, ten pre-trained models of CNN, namely ResNet18, ResNet50, ResNet101, ShuffleNet, GoogLeNet, Inception-V3, MobileNet-V2, Xception, Inception-ResNet-V2, and NASNet-Mobile are evaluated for the tasks of classification and prediction of defects in wooden structures. Each pre-trained CNN model is additionally trained and validated on an image dataset of 9000 images, equally divided into three classes: cracks, knots, and intact (undamaged). A smaller dataset of 300 images is separately used for testing purposes. Statistical parameters such as accuracy, precision, recall, and F1-score are computed for each CNN model. The Inception-V3 model proved to be the best CNN model for classifying defects in wooden structures based on the model’s accuracy, processing time and overall performance.</p><h2>Other Information</h2> <p> Published in: Case Studies in Construction Materials<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.cscm.2023.e02530" target="_blank">https://dx.doi.org/10.1016/j.cscm.2023.e02530</a></p>2023-12-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.cscm.2023.e02530https://figshare.com/articles/journal_contribution/Classification_of_defects_in_wooden_structures_using_pre-trained_models_of_convolutional_neural_network/25036391CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/250363912023-12-01T00:00:00Z |
| spellingShingle | Classification of defects in wooden structures using pre-trained models of convolutional neural network Rana Ehtisham (17820980) Engineering Civil engineering Materials engineering Information and computing sciences Computer vision and multimedia computation Machine learning Wooden defects Defects Classification Pre-trained Models CNN |
| status_str | publishedVersion |
| title | Classification of defects in wooden structures using pre-trained models of convolutional neural network |
| title_full | Classification of defects in wooden structures using pre-trained models of convolutional neural network |
| title_fullStr | Classification of defects in wooden structures using pre-trained models of convolutional neural network |
| title_full_unstemmed | Classification of defects in wooden structures using pre-trained models of convolutional neural network |
| title_short | Classification of defects in wooden structures using pre-trained models of convolutional neural network |
| title_sort | Classification of defects in wooden structures using pre-trained models of convolutional neural network |
| topic | Engineering Civil engineering Materials engineering Information and computing sciences Computer vision and multimedia computation Machine learning Wooden defects Defects Classification Pre-trained Models CNN |