DETECTING CRACKS AND SPALLING AUTOMATICALLY IN EXTREME EVENTS BY END-TO-END DEEP LEARNING FRAMEWORKS

16 citations as recorded by crossref.

1. Effect of different imaging modalities on the performance of a CNN: An experimental study on damage segmentation in infrared, visible, and fused images of concrete structures S. Pozzer et al.
2. A Framework for Multi-Class Surface Damage Segmentation, Quantification, and Severity Assessment of Reinforced Concrete Structures with GAN Data Augmentation J. Wang et al.
3. Datasets and processing methods for boosting visual inspection of civil infrastructure: A comprehensive review and algorithm comparison for crack classification, segmentation, and detection G. Yang et al.
4. Bridge inspection component registration for damage evolution E. Bianchi et al.
5. Visual structural inspection datasets E. Bianchi & M. Hebdon
6. A Study on Image Processing Using Artificial Neural Networks in Civil Engineering A. Andon & G. Covatariu
7. Transfer Learning in Earth Observation Data Analysis: A review A. Nowakowski et al.
8. Deep Learning-Based Automated Detection of Cracks in Historical Masonry Structures K. Hacıefendioğlu et al.
9. Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events Y. Bai et al.
10. Advances in artificial intelligence for structural health monitoring: A comprehensive review B. Spencer et al.
11. OASIS-Net: Morphological Attention Ensemble Learning for Surface Defect Detection Y. Hong & S. Yoo
12. Event-based civil infrastructure visual defect detection: ev-CIVIL dataset and benchmark U. Gamage et al.
13. Field monitoring and assessment of reinforced concrete building collapse using remote sensing and digital twins Y. Bai et al.
14. Integrating Machine Learning and Remote Sensing in Disaster Management: A Decadal Review of Post-Disaster Building Damage Assessment S. Al Shafian & D. Hu
15. Automated post-earthquake structural damage assessment of concrete buildings using a hybrid deep learning and rule-based framework on image datasets A. Türer et al.
16. Visual Concrete Bridge Defect Classification and Detection Using Deep Learning: A Systematic Review D. Amirkhani et al.