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Structural Defects Network (SDNET) 2018
许可协议: CC-BY-SA 4.0

Overview

Context

SDNET2018 is an annotated image dataset for training, validation, and benchmarking of artificial intelligence-based crack detection algorithms for concrete. SDNET2018 contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. The dataset includes cracks as narrow as 0.06 mm and as wide as 25 mm. The dataset also includes images with a variety of obstructions, including shadows, surface roughness, scaling, edges, holes, and background debris. SDNET2018 will be useful for the continued development of concrete crack detection algorithms based on deep learning convolutional neural networks, which are a subject of continued research in the field of structural health monitoring.

Content

230 images of cracked and non-cracked concrete surfaces (54 bridge decks, 72 walls, 104 pavements) are captured using a 16 MP Nikon digital camera. The bridge decks were located at the Utah State University system, material, and structural health (SMASH) laboratory. The inspected walls belong to Russell/Wanlass Performance Hall building located on USU campus The pavement images were acquired from the roads and sidewalks on USU campus. Each image is segmented into 256 × 256 px sub-images. Each sub-image is labelled as 'Cracked' if there was a crack in the sub-image or 'Non-cracked' if there was not a crack.

Acknowledgements

Utah State University Campus, Logan, Utah, USA

Inspiration

S. Dorafshan and M. Maguire, "Autonomous detection of concrete cracks on bridge decks and fatigue cracks on steel members," in Digital Imaging 2017, Mashantucket, CT, 2017.
S. Dorafshan, M. Maguire and M. Chang, "Comparing automated image-based crack detection techniques in spatial and frequency domains," in Proceedings of the 26th American Society of Nondestructive Testing Research Symposium, Jacksonville, FL, 2017.
S. Dorafshan, M. Maguire, N. Hoffer and C. Coopmans, "Challenges in bridge inspection using small unmanned aerial systems: Results and lessons learned," in Proceedings of the 2017 International Conference on Unmanned Aircraft Systems, Miami, FL, 2017.
S. Dorafshan, C. Coopmans, R. J. Thomas and M. Maguire, "Deep Learning Neural Networks for sUAS-Assisted Structural Inspections, Feasibility and Application," in ICUAS 2018, Dallas, TX, 2018.
S. Dorafshan, M. Maguire and X. Qi, "Automatic Surface Crack Detection in Concrete Structures Using OTSU Thresholding and Morphological Operations," Utah State University, Logan, Utah, USA, 2016.
S. Dorafshan, J. R. Thomas and M. Maguire, "Comparison of Deep Learning Convolutional Neural Networks and Edge Detectors for Image-Based Crack Detection in Concrete," Submitted to Journal of Construction and Building Materials, 2018.
S. Dorafshan, R. Thomas and M. Maguire, "Image Processing Algorithms for Vision-based Crack Detection in Concrete Structures," Submitted to Advanced Concrete Technology, 2018.

数据概要
数据格式
image,
数据量
56.092K
文件大小
63.19MB
发布方
whoami-as
| 数据量 56.092K | 大小 63.19MB
Structural Defects Network (SDNET) 2018
许可协议: CC-BY-SA 4.0

Overview

Context

SDNET2018 is an annotated image dataset for training, validation, and benchmarking of artificial intelligence-based crack detection algorithms for concrete. SDNET2018 contains over 56,000 images of cracked and non-cracked concrete bridge decks, walls, and pavements. The dataset includes cracks as narrow as 0.06 mm and as wide as 25 mm. The dataset also includes images with a variety of obstructions, including shadows, surface roughness, scaling, edges, holes, and background debris. SDNET2018 will be useful for the continued development of concrete crack detection algorithms based on deep learning convolutional neural networks, which are a subject of continued research in the field of structural health monitoring.

Content

230 images of cracked and non-cracked concrete surfaces (54 bridge decks, 72 walls, 104 pavements) are captured using a 16 MP Nikon digital camera. The bridge decks were located at the Utah State University system, material, and structural health (SMASH) laboratory. The inspected walls belong to Russell/Wanlass Performance Hall building located on USU campus The pavement images were acquired from the roads and sidewalks on USU campus. Each image is segmented into 256 × 256 px sub-images. Each sub-image is labelled as 'Cracked' if there was a crack in the sub-image or 'Non-cracked' if there was not a crack.

Acknowledgements

Utah State University Campus, Logan, Utah, USA

Inspiration

S. Dorafshan and M. Maguire, "Autonomous detection of concrete cracks on bridge decks and fatigue cracks on steel members," in Digital Imaging 2017, Mashantucket, CT, 2017.
S. Dorafshan, M. Maguire and M. Chang, "Comparing automated image-based crack detection techniques in spatial and frequency domains," in Proceedings of the 26th American Society of Nondestructive Testing Research Symposium, Jacksonville, FL, 2017.
S. Dorafshan, M. Maguire, N. Hoffer and C. Coopmans, "Challenges in bridge inspection using small unmanned aerial systems: Results and lessons learned," in Proceedings of the 2017 International Conference on Unmanned Aircraft Systems, Miami, FL, 2017.
S. Dorafshan, C. Coopmans, R. J. Thomas and M. Maguire, "Deep Learning Neural Networks for sUAS-Assisted Structural Inspections, Feasibility and Application," in ICUAS 2018, Dallas, TX, 2018.
S. Dorafshan, M. Maguire and X. Qi, "Automatic Surface Crack Detection in Concrete Structures Using OTSU Thresholding and Morphological Operations," Utah State University, Logan, Utah, USA, 2016.
S. Dorafshan, J. R. Thomas and M. Maguire, "Comparison of Deep Learning Convolutional Neural Networks and Edge Detectors for Image-Based Crack Detection in Concrete," Submitted to Journal of Construction and Building Materials, 2018.
S. Dorafshan, R. Thomas and M. Maguire, "Image Processing Algorithms for Vision-based Crack Detection in Concrete Structures," Submitted to Advanced Concrete Technology, 2018.

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