Integrated pixel-level CNN-FCN crack detection via photogrammetric 3D texture mapping of concrete structures
Although multiple learning-based crack detection systems show promising results in detecting cracks with pixel accuracy on individual images, few effectively enable inspection of larger structures. This paper thereby proposes an advanced inspection reporting system based on an integrated CNN-FCN cra...
| Main Authors: | Chaiyasarn, K., Buatik, A., Mohamad, H., Zhou, M., Kongsilp, S., Poovarodom, N. |
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| Format: | Article |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.33348 / |
| Published: |
Elsevier B.V.
2022
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131460599&doi=10.1016%2fj.autcon.2022.104388&partnerID=40&md5=314e85acc5fdd91ac70a6289be5d41ce http://eprints.utp.edu.my/33348/ |
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| Summary: |
Although multiple learning-based crack detection systems show promising results in detecting cracks with pixel accuracy on individual images, few effectively enable inspection of larger structures. This paper thereby proposes an advanced inspection reporting system based on an integrated CNN-FCN crack detection system applied on the texture space of a footing, enabling crack inspection and display for larger structures. The system, a Convolutional Neural Network (CNN) and a Fully Convolutional Network (FCN), segments cracks at the pixel-level on the texture space, acquired from a 3D model created with photogrammetry techniques. Firstly, the trained CNN is employed to detect crack patches, then imported to the trained FCN system to segment cracks at the pixel-level, and a crack map is then generated which is projected onto a 3D model. This system indicates promising results for footing textures as represented by: Accuracy (99.88), Precision (82.2), Recall (90.2), and F1 Score (86.01). © 2022 |
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