DOI: 10.17603/ds2-wneq-sg96
title: Detection of Cracking and Spalling Damage in Buildings and Bridges.
title: in Detection of Cracking and Spalling Damage in Buildings and Bridges

description: This is part of an NSF project (the information is this link: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036193&HistoricalAwards=false}. In this project, over 2,200 images are used to label cracks and spalling on buildings and bridges damaged during extreme events. The data format is MS-COCO when the boundaries of the damage are manually drawn with polygon lines. The training and validation datasets are uploaded for the research community.  The reference GitHub link is here: https://github.com/Bai426/Damage-Detection-with-COCO-data-and-Mask-R-CNN. If you think the data is useful to your research, please cite the following publications:
[1] Bai Y., Zha B., Sezen H., Yilmaz A. (2023). Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events. Structural Health Monitoring. 2023;22(1):338-352. doi:10.1177/14759217221083649
[2] Bai Y., Sezen H., Yilmaz A. (2021). "End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales," 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021, pp. 6640-6647, doi: 10.1109/ICPR48806.2021.9413041.
[3] Bai, Y., Sezen, H., Yilmaz, A. (2021). Detecting cracks and spalling automatically in extreme events by end-to-end deep learning frameworks. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 161-168.
[4] Bai Y. (2022). Deep learning with vision-based technologies for structural damage detection and health monitoring. PhD dissertation at Ohio State University.

description: This is part of an NSF project (the information is this link: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036193&HistoricalAwards=false}. In this project, over 2,200 images are used to label cracks and spalling on buildings and bridges damaged during extreme events. The data format is MS-COCO when the boundaries of the damage are manually drawn with polygon lines. The training and validation datasets are uploaded for the research community.  The reference GitHub link is here: https://github.com/Bai426/Damage-Detection-with-COCO-data-and-Mask-R-CNN. If you think the data is useful to your research, please cite the following publications:
[1] Bai Y., Zha B., Sezen H., Yilmaz A. (2023). Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events. Structural Health Monitoring. 2023;22(1):338-352. doi:10.1177/14759217221083649
[2] Bai Y., Sezen H., Yilmaz A. (2021). End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales, 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 2021, pp. 6640-6647, doi: 10.1109/ICPR48806.2021.9413041.
[3] Bai, Y., Sezen, H., Yilmaz, A. (2021). Detecting cracks and spalling automatically in extreme events by end-to-end deep learning frameworks. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 161-168.
[4] Bai Y. (2022). Deep learning with vision-based technologies for structural damage detection and health monitoring. PhD dissertation at Ohio State university.