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SDNET2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural
Sattar Dorafshan1, Robert J Thomas2, Marc Maguire1
1Department of Civil and Environmental Engineering, Utah State University, Logan, Utah. USA.
Data in Brief
|December 4, 2018
Summary
SDNET2018 is a new dataset for training artificial intelligence to detect concrete cracks. It features over 56,000 images, aiding the development of structural health monitoring technologies.
Area of Science:
- Civil Engineering
- Computer Science
- Artificial Intelligence
Background:
- Concrete structures are prone to cracking, necessitating effective detection methods.
- Structural health monitoring (SHM) relies on accurate crack identification for safety and maintenance.
- Existing datasets may lack the diversity and scale required for robust AI-based crack detection.
Purpose of the Study:
- To introduce SDNET2018, a comprehensive annotated image dataset for AI-based concrete crack detection.
- To provide a benchmark for evaluating deep convolutional neural network (DCNN) algorithms in crack detection.
- To facilitate research and development in automated structural health monitoring.
Main Methods:
- Compilation of over 56,000 annotated images of cracked and non-cracked concrete elements (bridge decks, walls, pavements).
- Inclusion of images with various crack widths (0.06 mm to 25 mm) and obstructions (shadows, roughness, scaling, debris).
- Development and application of a crack detection algorithm based on the AlexNet DCNN architecture for benchmarking.
Main Results:
- The SDNET2018 dataset encompasses a wide range of crack characteristics and image complexities.
- Benchmark results demonstrate the performance of an AlexNet-based DCNN on the SDNET2018 dataset.
- The dataset is suitable for training and validating advanced AI models for concrete crack analysis.
Conclusions:
- SDNET2018 provides a valuable resource for advancing AI-driven concrete crack detection.
- The dataset supports the development of more reliable structural health monitoring systems.
- Availability of SDNET2018 at https://doi.org/10.15142/T3TD19 promotes further research and innovation.
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