Journal of the Korean GEO-environmental Society (한국지반환경공학회 논문집)

Korean Geo-Environmental Society (한국지반환경공학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Crack Detection in Asphalt Road Pavement Using Small Deep Learning

스몰 딥러닝을 이용한 아스팔트 도로 포장의 균열 탐지에 관한 연구

  • Ji, Bongjun (Industrial and Management Engineering, Pohang University of Science and Technology)
  • Received : 2021.08.11
  • Accepted : 2021.09.24
  • Published : 2021.10.01
Download PDF
⟨ Previous Next ⟩

Abstract

Cracks in asphalt pavement occur due to changes in weather or impact from vehicles, and if cracks are left unattended, the life of the pavement may be shortened, and various accidents may occur. Therefore, studies have been conducted to detect cracks through images in order to quickly detect cracks in the asphalt pavement automatically and perform maintenance activity. Recent studies adopt machine-learning models for detecting cracks in asphalt road pavement using a Convolutional Neural Network. However, their practical use is limited because they require high-performance computing power. Therefore, this paper proposes a framework for detecting cracks in asphalt road pavement by applying a small deep learning model applicable to mobile devices. The small deep learning model proposed through the case study was compared with general deep learning models, and although it was a model with relatively few parameters, it showed similar performance to general deep learning models. The developed model is expected to be embedded and used in mobile devices or IoT for crack detection in asphalt pavement.

아스팔트 포장의 균열은 날씨의 변화나 차량에 의한 충격으로 발생하며, 균열을 방치할 경우 포장 수명이 단축되고 각종 사고를 불러 일으킬 수 있다. 따라서 아스팔트 도로 포장의 균열을 빠르게 감지하여 보수조치를 취하기 위하여 이미지를 통해 균열을 자동으로 탐지하기 위한 연구들이 지속되어 왔다. 특히 최근들어 Convolutional Neural Network를 사용하여 아스팔트 도로 포장의 균열을 탐지하려는 모델들이 많이 연구되고 있으나, 고성능의 컴퓨팅 파워를 요구하기 때문에 실제 활용에는 한계가 있다. 이에 본 논문에서는 모바일 기기에 적용 가능한 스몰 딥러닝 모델을 적용하여 아스팔트 도로 포장의 균열을 탐지하는 모델의 개발을 위한 프레임워크를 제안한다. 사례연구를 통해 제안한 스몰 딥러닝 모델은 일반적인 딥러닝 모델들과 비교 연구되었으며, 상대적으로 적은 파라미터를 가지는 모델임에도 일반적인 딥러닝 모델들과 유사한 성능을 보였다. 개발된 모델은 모바일 기기나 IoT에 임베디드 되어 사용될 수 있을 것으로 기대된다.

Keywords

References

  1. Cha, Y. J., Choi, W., Suh, G., Mahmoudkhani, S. and Buyukozturk, O. (2017), Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types, Computer-Aided Civil and Infrastructure Engineering, Vol. 33, No.9, pp. 731~747.
  2. Choi, S. H., Do, M. S., You, S. H. and Cho, C. S. (2018), Determination of visual based asphalt pavement crack condition using deep learning, International Journal of Highway Engineering, Vol. 20, No. 5, pp. 75~83 (In Korean). https://doi.org/10.7855/IJHE.2018.20.5.075
  3. Chun, P. J., Yamane, T. and Tsuzuki, Y. (2021), Automatic detection of cracks in asphalt pavement using deep learning to overcome weaknesses in Images and GIS Visualization, Applied Sciences, Vol. 11, No. 3, pp. 892. https://doi.org/10.3390/app11030892
  4. Dorafshan, S., Thomas, R. J. and Maguire, M. (2018), Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete, Construction and Building Materials, Vol. 186, pp. 1031~1045. https://doi.org/10.1016/j.conbuildmat.2018.08.011
  5. He, K., Zhang, X., Ren, S. and Sun, J. (2016), Deep residual learning for image recognition, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770~778.
  6. Balaji, A. J., Balaji, G. T., Dinesh, M. S., Binoy, N. and Ram, D. S. (2019), Asphalt crack dataset, Mendeley Data, V2.
  7. Kim, J. H., Kim, J. R. and Moon, H. C. (2008), Development of pavement distress survey system, Journal of Korean Society of Road Engineers, pp. 475~484 (In Korean).
  8. Koch, C. and Brilakis, I. (2011), Pothole detection in asphalt pavement images, Advanced Engineering Informatics, Vol. 25, No. 3, pp. 507~515. https://doi.org/10.1016/j.aei.2011.01.002
  9. Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2012), Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, 25, 1097~1105.
  10. Kumar, A., Kumar, A., Jha, A. K. and Trivedi, A. (2020), Crack detection of structures using deep learning framework, In 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) pp. 526~533.
  11. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W. and Jackel, L. D. (1989), Backpropagation applied to handwritten zip code recognition, Neural Computation, Vol. 1, No. 4, pp. 541~551. https://doi.org/10.1162/neco.1989.1.4.541
  12. Nhat-Duc, Hoang, Quoc-Lam Nguyen and Van-Duc Tran. (2018), Automatic recognition of asphalt pavement cracks using metaheuristic optimized edge detection algorithms and convolution neural network, Automation in Construction Vol. 94, pp. 203~213. https://doi.org/10.1016/j.autcon.2018.07.008
  13. Simonyan, K. and Zisserman, A. (2014), Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556.
  14. Weng, X., Huang, Y. and Wang, W. (2019), Segment-based pavement crack quantification, Automation in Construction, Vol. 105, pp. 102819. https://doi.org/10.1016/j.autcon.2019.04.014