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Smart Structures and Systems Volume 31, Number 4, April 2023 , pages 325-334 DOI: https://doi.org/10.12989/sss.2023.31.4.325 |
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A hierarchical semantic segmentation framework for computer vision-based bridge damage detection |
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Jingxiao Liu, Yujie Wei, Bingqing Chen and Hae Young Noh
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| Abstract | ||
| Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model. | ||
| Key Words | ||
| bridge health monitoring; computer vision; damage detection; semantic segmentation | ||
| Address | ||
| (1) Jingxiao Liu, Hae Young Noh: Department of Civil and Environmental Engineering, Stanford University, Stanford, CA, USA; (2) Yujie Wei, Bingqing Chen: Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. | ||