Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasets
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908543155699712 |
|---|---|
| author | Zhang, Xinan Wang, Haolin Hsieh, Yung-An Yang, Zhongyu Yezzi, Anthony Tsai, Yi-Chang |
| author_facet | Zhang, Xinan Wang, Haolin Hsieh, Yung-An Yang, Zhongyu Yezzi, Anthony Tsai, Yi-Chang |
| contents | Crack detection plays a crucial role in civil infrastructures, including inspection of pavements, buildings, etc., and deep learning has significantly advanced this field in recent years. While numerous technical and review papers exist in this domain, emerging trends are reshaping the landscape. These shifts include transitions in learning paradigms (from fully supervised learning to semi-supervised, weakly-supervised, unsupervised, few-shot, domain adaptation and fine-tuning foundation models), improvements in generalizability (from single-dataset performance to cross-dataset evaluation), and diversification in dataset acquisition (from RGB images to specialized sensor-based data). In this review, we systematically analyze these trends and highlight representative works. Additionally, we introduce a new annotated dataset collected with 3D laser scans, 3DCrack, to support future research and conduct extensive benchmarking experiments to establish baselines for commonly used deep learning methodologies, including recent foundation models. Our findings provide insights into the evolving methodologies and future directions in deep learning-based crack detection. Project page: https://github.com/nantonzhang/Awesome-Crack-Detection |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10256 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasets Zhang, Xinan Wang, Haolin Hsieh, Yung-An Yang, Zhongyu Yezzi, Anthony Tsai, Yi-Chang Computer Vision and Pattern Recognition Crack detection plays a crucial role in civil infrastructures, including inspection of pavements, buildings, etc., and deep learning has significantly advanced this field in recent years. While numerous technical and review papers exist in this domain, emerging trends are reshaping the landscape. These shifts include transitions in learning paradigms (from fully supervised learning to semi-supervised, weakly-supervised, unsupervised, few-shot, domain adaptation and fine-tuning foundation models), improvements in generalizability (from single-dataset performance to cross-dataset evaluation), and diversification in dataset acquisition (from RGB images to specialized sensor-based data). In this review, we systematically analyze these trends and highlight representative works. Additionally, we introduce a new annotated dataset collected with 3D laser scans, 3DCrack, to support future research and conduct extensive benchmarking experiments to establish baselines for commonly used deep learning methodologies, including recent foundation models. Our findings provide insights into the evolving methodologies and future directions in deep learning-based crack detection. Project page: https://github.com/nantonzhang/Awesome-Crack-Detection |
| title | Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasets |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.10256 |