Improving Post-Earthquake Crack Detection using Semi-Synthetic Generated Images
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915312242262016 |
|---|---|
| author | Dondi, Piercarlo Gullotti, Alessio Inchingolo, Michele Senaldi, Ilaria Casarotti, Chiara Lombardi, Luca Piastra, Marco |
| author_facet | Dondi, Piercarlo Gullotti, Alessio Inchingolo, Michele Senaldi, Ilaria Casarotti, Chiara Lombardi, Luca Piastra, Marco |
| contents | Following an earthquake, it is vital to quickly evaluate the safety of the impacted areas. Damage detection systems, powered by computer vision and deep learning, can assist experts in this endeavor. However, the lack of extensive, labeled datasets poses a challenge to the development of these systems. In this study, we introduce a technique for generating semi-synthetic images to be used as data augmentation during the training of a damage detection system. We specifically aim to generate images of cracks, which are a prevalent and indicative form of damage. The central concept is to employ parametric meta-annotations to guide the process of generating cracks on 3D models of real-word structures. The governing parameters of these meta-annotations can be adjusted iteratively to yield images that are optimally suited for improving detectors' performance. Comparative evaluations demonstrated that a crack detection system trained with a combination of real and semi-synthetic images outperforms a system trained on real images alone. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05042 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Post-Earthquake Crack Detection using Semi-Synthetic Generated Images Dondi, Piercarlo Gullotti, Alessio Inchingolo, Michele Senaldi, Ilaria Casarotti, Chiara Lombardi, Luca Piastra, Marco Computer Vision and Pattern Recognition Artificial Intelligence Following an earthquake, it is vital to quickly evaluate the safety of the impacted areas. Damage detection systems, powered by computer vision and deep learning, can assist experts in this endeavor. However, the lack of extensive, labeled datasets poses a challenge to the development of these systems. In this study, we introduce a technique for generating semi-synthetic images to be used as data augmentation during the training of a damage detection system. We specifically aim to generate images of cracks, which are a prevalent and indicative form of damage. The central concept is to employ parametric meta-annotations to guide the process of generating cracks on 3D models of real-word structures. The governing parameters of these meta-annotations can be adjusted iteratively to yield images that are optimally suited for improving detectors' performance. Comparative evaluations demonstrated that a crack detection system trained with a combination of real and semi-synthetic images outperforms a system trained on real images alone. |
| title | Improving Post-Earthquake Crack Detection using Semi-Synthetic Generated Images |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2412.05042 |