Parameter analysis for augmentation of tunnel concrete crack image data based on generative AI

Seungbo Shim

doi:10.9711/KTAJ.2024.26.6.633

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2024 Vol.26, Issue 6 Preview Page Next Page

Research Paper

Parameter analysis for augmentation of tunnel concrete crack image data based on generative AI 생성형 인공지능 기반 터널 콘크리트 균열 영상 데이터의 증강을 위한 파라미터 분석

30 November 2024. pp. 633-645

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Abstract

To maintain the safety of aging infrastructure, continuous management is essential, and this applies equally to concrete structures inside tunnels. The health of tunnel structures is managed through regular inspections and precise examination techniques. Traditional inspection methods are manpower-based, where workers visit the site in person to visually assess and record the condition. As a result, the condition of the structure is often determined based on subjective and experiential judgment. To address these issues and enhance the objectivity and reliability of inspection results, methods using high-resolution cameras and deep learning are being actively researched. Neural network model-based algorithms for detecting cracks in tunnel structures, in particular, have demonstrated high accuracy. However, such deep learning technology relies on the premise that a large amount of training image data is available. In reality, damage images such as cracks are not easily found, and collecting them involves significant costs and time. To address this challenge, this study proposes a method for augmenting crack image data using generative AI. Additionally, parameter analysis was conducted to generate crack images resembling real ones, resulting in a generative model with a performance of 31.73 Fréchet Inception Distance. This method is expected to be applied in conjunction with crack detection training methods, contributing to the improved accuracy and reliability of maintenance inspections.

Keywords

Tunnel maintenance

Training data augmentation

Generative AI

Crack detection

Crack image

노후 인프라 구조물을 안전하게 유지하기 위해서는 지속적인 관리가 필요하며, 이는 터널 내부의 콘크리트 구조물에도 동일하게 적용된다. 터널 구조물의 건전성 관리는 정기적인 점검과 정밀한 검사 기술을 통해 이루어진다. 기존의 검사 방법은 인력 기반으로, 작업자가 직접 현장을 방문하여 육안으로 상태를 확인하고 기록하는 방식이다. 이로 인해 구조물 상태는 경험적이고 주관적인 판단에 의해 결정될 수밖에 없는 상황이다. 이러한 문제를 개선하고 점검 결과의 객관성과 신뢰성을 높이기 위해 고해상도 카메라와 딥러닝을 활용한 방법이 활발히 연구되고 있다. 특히, 터널 구조물에 발생하는 균열을 탐지하는 신경망 모델 기반의 알고리즘은 높은 정확도를 보여주고 있다. 그러나 이러한 딥러닝 기술은 다수의 훈련 영상 데이터가 확보되었다는 전제가 필요하다. 현실적으로 균열과 같은 손상 영상은 주변에서 흔히 찾아볼 수 없고, 이를 확보하는 데 많은 비용과 시간이 소요된다. 이러한 문제를 해결하기 위해 본 연구에서는 생성형 AI를 이용해 균열 영상 데이터를 증강하는 방법을 제안하였다. 또한, 실제와 유사한 균열 영상을 생성하기 위한 파라미터 분석을 완료하였고, 그 결과 Fréchet Inception Distance가 31.73의 값을 나타내는 성능의 생성모델을 확보하였다. 이러한 방법은 향후 균열 탐지 훈련 방법과 연계하여 유지관리 점검의 정확성과 신뢰성을 높이는 기술로 활용될 것으로 기대된다.

키워드

터널 유지관리

훈련 데이터 증강

생성형 AI

균열 탐지

균열 영상

References

Bianchi, E., Hebdon, M. (2021), "Concrete crack conglomerate dataset", University Libraries, Virginia Tech, Dataset.

Chen, X., Yuan, Y., Zeng, G., Wang, J. (2021), "Semi-supervised semantic segmentation with cross pseudo supervision", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, pp. 2613-2622.

10.1109/CVPR46437.2021.00264

Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y. (2014), "Generative adversarial nets", Proceedings of the Advances in Neural Information Processing Systems (NIPS), Montreal, Canada, pp. 2672-2680.

Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S. (2017), "Gans trained by a two time-scale update rule converge to a local nash equilibrium", Proceedings of the Advances in Neural Information Processing Systems, Vol. 30, Long Beach, CA, USA, 6626-6637.

Hsieh, Y.A., Tsai, Y.J. (2020), "Machine learning for crack detection: review and model performance comparison", Journal of Computing in Civil Engineering, Vol. 34, No. 5, 04020038.

10.1061/(ASCE)CP.1943-5487.0000918

Huang, B., Kang, F., Li, X., Zhu, S. (2024), "Underwater dam crack image generation based on unsupervised image-to-image translation", Automation in Construction, Vol. 163, 105430.

10.1016/j.autcon.2024.105430

Lee, S.Y., Lee, S.H., Shin, D.I., Son, Y.K., Han, C.S. (2007), "Development of an inspection system for cracks in a concrete tunnel lining", Canadian Journal of Civil Engineering, Vol. 34, No. 8, pp. 966-975.

10.1139/l07-008

Lei, M., Liu, L., Shi, C., Tan, Y., Lin, Y., Wang, W. (2021), "A novel tunnel-lining crack recognition system based on digital image technology", Tunnelling and Underground Space Technology, Vol. 108, 103724.

10.1016/j.tust.2020.103724

Li, G., Ma, B., He, S., Ren, X., Liu, Q. (2020), "Automatic tunnel crack detection based on U-Net and a convolutional neural network with alternately updated clique", Sensors, Vol. 20, No. 3, 717.

10.3390/s2003071732012919PMC7038519

Li, S., Zhao, X. (2023), "High-resolution concrete damage image synthesis using conditional generative adversarial network", Automation in Construction, Vol. 147, 104739.

10.1016/j.autcon.2022.104739

Liu, J., Zhou, Q., Qiang, Y., Kang, B., Wu, X., Zheng, B. (2020), "FDDWNet: a lightweight convolutional neural network for real-time semantic segmentation", Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Barcelona, Spain, pp. 2373-2377.

10.1109/ICASSP40776.2020.9053838

Lynch, J.P., Farrar, C.R., Michaels, J.E. (2016), "Structural health monitoring: technological advances to practical implementations [scanning the issue]", Proceedings of the IEEE, Vol. 104, No. 8, pp. 1508-1512.

10.1109/JPROC.2016.2588818

Maeda, H., Kashiyama, T., Sekimoto, Y., Seto, T., Omata, H. (2021), "Generative adversarial network for road damage detection", Computer-Aided Civil and Infrastructure Engineering, Vol. 36, No. 1, pp. 47-60.

10.1111/mice.12561

Munawar, H.S., Hammad, A.W.A., Haddad, A., Soares, C.A.P., Waller, S.T. (2021), "Image-based crack detection methods: a review", Infrastructures, Vol. 6, No. 8, 115.

10.3390/infrastructures6080115

Phares, B.M., Washer, G.A., Rolander, D.D., Graybeal, B.A., Moore, M. (2004), "Routine highway bridge inspection condition documentation accuracy and reliability", Journal of Bridge Engineering, Vol. 9, No. 4, pp. 403-413.

10.1061/(ASCE)1084-0702(2004)9:4(403)

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B. (2022), "High-resolution image synthesis with latent diffusion models", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, pp. 10684-10695.

10.1109/CVPR52688.2022.01042

Shim, S. (2022), "CycleGAN based translation method between asphalt and concrete crack images for data augmentation", The Journal of The Korea Institute of Intelligent Transport Systems, Vol. 21, No. 5, pp. 171-182.

10.12815/kits.2022.21.5.171

Shim, S. (2024), "Self-training approach for crack detection using synthesized crack images based on conditional generative adversarial network", Computer-Aided Civil and Infrastructure Engineering, Vol. 39, No. 7, pp. 1019-1041.

10.1111/mice.13119

Zhang, K., Zhang, Y., Cheng, H.D. (2020), "Self-supervised structure learning for crack detection based on cycle-consistent generative adversarial networks", Journal of Computing in Civil Engineering, Vol. 34, No. 3, 04020004.

10.1061/(ASCE)CP.1943-5487.0000883

Information

Publisher :Korean Tunneling and Underground Space Association
Publisher(Ko) :한국터널지하공간학회
Journal Title :Journal of Korean Tunnelling and Underground Space Association
Journal Title(Ko) :한국터널지하공간학회 논문집
Volume : 26
No :6
Pages :633-645
Received Date : 2024-09-12
Revised Date : 2024-10-21
Accepted Date : 2024-10-28
DOI :https://doi.org/10.9711/KTAJ.2024.26.6.633

Journal of Korean Tunnelling and Underground Space Association ISSN:2233-8292(Print) 2287-4747(Online) 한국터널지하공간학회 논문집

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