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Smart Structures and Systems Volume 30, Number 1, July 2022 , pages 17-34 DOI: https://doi.org/10.12989/sss.2022.30.1.017 |
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Semantic crack-image identification framework for steel structures using atrous convolution-based Deeplabv3+ Network |
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Quoc-Bao Ta, Ngoc-Loi Dang, Yoon-Chul Kim, Hyeon-Dong Kam and Jeong-Tae Kim
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| Abstract | ||
| For steel structures, fatigue cracks are critical damage induced by long-term cycle loading and distortion effects. Vision-based crack detection can be a solution to ensure structural integrity and performance by continuous monitoring and nondestructive assessment. A critical issue is to distinguish cracks from other features in captured images which possibly consist of complex backgrounds such as handwritings and marks, which were made to record crack patterns and lengths during periodic visual inspections. This study presents a parametric study on image-based crack identification for orthotropic steel bridge decks using captured images with complicated backgrounds. Firstly, a framework for vision-based crack segmentation using the atrous convolution-based Deeplapv3+ network (ACDN) is designed. Secondly, features on crack images are labeled to build three databanks by consideration of objects in the backgrounds. Thirdly, evaluation metrics computed from the trained ACDN models are utilized to evaluate the effects of obstacles on crack detection results. Finally, various training parameters, including image sizes, hyper-parameters, and the number of training images, are optimized for the ACDN model of crack detection. The result demonstrated that fatigue cracks could be identified by the trained ACDN models, and the accuracy of the crack-detection result was improved by optimizing the training parameters. It enables the applicability of the vision-based technique for early detecting tiny fatigue cracks in steel structures. | ||
| Key Words | ||
| atrous convolution; Deeplabv3+ network; fatigue crack; image processing technique; semantic segmentation; steel structures | ||
| Address | ||
| (1) Quoc-Bao Ta, Hyeon-Dong Kam, Jeong-Tae Kim: Department of Ocean Eng., Pukyong National University, Nam-gu, Busan 48513, Korea; (2) Ngoc-Loi Dang: Urban Infrastructure Faculty, Mien Tay Construction University, Vinh Long 890000, Vietnam; (3) Yoon-Chul Kim: Department of Civil and Environmental Eng., Yonsei University, Seodaemun-gu, Seoul 03722, Korea. | ||