Block pavement and distress segmentation using deep learning models
DC Field | Value | Language |
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dc.contributor.author | Denu, Eskndir Getachew | - |
dc.contributor.author | Cho, Yoon-Ho | - |
dc.date.accessioned | 2024-07-12T05:00:45Z | - |
dc.date.available | 2024-07-12T05:00:45Z | - |
dc.date.issued | 2024-07 | - |
dc.identifier.issn | 2364-4176 | - |
dc.identifier.issn | 2364-4184 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/74709 | - |
dc.description.abstract | Block pavements require efficient distress detection and segmentation methods for quality control and pavement management systems. This research proposes TransUNet, a hybrid model for block and distress segmentation, combining convolutional neural networks (CNNs) and Vision transformers. The model adopts transfer learning to achieve accurate block segmentation, pre-training the model on a large wall image dataset and then fine-tuning it on a smaller set of block pavement images. This approach yields promising results with 77% intersection over union (IoU), 96.8% precision, 99.5% recall, and 98.1% F1-score, surpassing conventional CNN-based models, UNet and UNet + + . The use of transfer learning not only enhances accuracy but also significantly reduces training time and computational resources, as it eliminates the need for a large dataset. For the block distress segmentation model, the proposed hybrid TransUNet model obtained a mIoU of 71.3% outperforming CNN-based models. The CNN models often struggle to handle the diverse distress types commonly found in block pavements, resulting in sub-optimal distress segmentation outcomes. By automating block and distress segmentation, the proposed models contribute to efficient maintenance planning and the development of sustainable infrastructure. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | SPRINGER INT PUBL AG | - |
dc.title | Block pavement and distress segmentation using deep learning models | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s41062-024-01533-2 | - |
dc.identifier.bibliographicCitation | INNOVATIVE INFRASTRUCTURE SOLUTIONS, v.9, no.7 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 001242945200002 | - |
dc.identifier.scopusid | 2-s2.0-85195828704 | - |
dc.citation.number | 7 | - |
dc.citation.title | INNOVATIVE INFRASTRUCTURE SOLUTIONS | - |
dc.citation.volume | 9 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | TransUNet | - |
dc.subject.keywordAuthor | Block pavement | - |
dc.subject.keywordAuthor | Distress detection | - |
dc.subject.keywordAuthor | Convolutional neural networks | - |
dc.subject.keywordAuthor | Transformers | - |
dc.subject.keywordAuthor | Transfer learning | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Civil | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | esci | - |
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