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Smart Structures and Systems Volume 29, Number 1, January 2022 , pages 237-250 DOI: https://doi.org/10.12989/sss.2022.29.1.237 |
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Synthetic data augmentation for pixel-wise steel fatigue crack identification using fully convolutional networks |
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Guanghao Zhai, Yasutaka Narazaki, Shuo Wang, Shaik Althaf V. Shajihan and Billie F. Spencer Jr.
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| Abstract | ||
| Structural health monitoring (SHM) plays an important role in ensuring the safety and functionality of critical civil infrastructure. In recent years, numerous researchers have conducted studies to develop computer vision and machine learning techniques for SHM purposes, offering the potential to reduce the laborious nature and improve the effectiveness of field inspections. However, high-quality vision data from various types of damaged structures is relatively difficult to obtain, because of the rare occurrence of damaged structures. The lack of data is particularly acute for fatigue crack in steel bridge girder. As a result, the lack of data for training purposes is one of the main issues that hinders wider application of these powerful techniques for SHM. To address this problem, the use of synthetic data is proposed in this article to augment real-world datasets used for training neural networks that can identify fatigue cracks in steel structures. First, random textures representing the surface of steel structures with fatigue cracks are created and mapped onto a 3D graphics model. Subsequently, this model is used to generate synthetic images for various lighting conditions and camera angles. A fully convolutional network is then trained for two cases: (1) using only real-word data, and (2) using both synthetic and real-word data. By employing synthetic data augmentation in the training process, the crack identification performance of the neural network for the test dataset is seen to improve from 35% to 40% and 49% to 62% for intersection over union (IoU) and precision, respectively, demonstrating the efficacy of the proposed approach. | ||
| Key Words | ||
| fully convolutional networks; semantic segmentation; steel fatigue crack; synthetic data | ||
| Address | ||
| (1) Guanghao Zhai, Shuo Wang, Shaik Althaf V. Shajihan, Billie F. Spencer Jr.: Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA; (2) Yasutaka Narazaki: Zhejiang University – University of Illinois at Urbana-Champaign Institute, Zhejiang University, Haining, Zhejiang 314400, China. | ||