Deep Learning Models for Autonomous Crack Detection System


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 5, pp. 161-168, May. 2021
https://doi.org/10.3745/KTSDE.2021.10.5.161,   PDF Download:
Keywords: Surface Inspection, Crack Detection, computer vision, Deep Learning
Abstract

Cracks affect the robustness of infrastructures such as buildings, bridge, pavement, and pipelines. This paper presents an automated crack detection system which detect cracks in diverse surfaces. We first constructed the combined crack dataset, consists of multiple crack datasets in diverse domains presented in prior studies. Then, state-of-the-art deep learning models in computer vision tasks including VGG, ResNet, WideResNet, ResNeXt, DenseNet, and EfficientNet, were used to validate the performance of crack detection. We divided the combined dataset into train (80%) and test set (20%) to evaluate the employed models. DenseNet121 showed the highest accuracy at 96.20% with relatively low number of parameters compared to other models. Based on the validation procedures of the advanced deep learning models in crack detection task, we shed light on the cost-effective automated crack detection system which can be applied to different surfaces and structures with low computing resources.


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Cite this article
[IEEE Style]
J. HongGeun, K. Jina, H. Syjung, K. Dogun, P. Eunil, K. Y. Seok, R. S. Ki, "Deep Learning Models for Autonomous Crack Detection System," KIPS Transactions on Software and Data Engineering, vol. 10, no. 5, pp. 161-168, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.5.161.

[ACM Style]
Ji HongGeun, Kim Jina, Hwang Syjung, Kim Dogun, Park Eunil, Kim Young Seok, and Ryu Seung Ki. 2021. Deep Learning Models for Autonomous Crack Detection System. KIPS Transactions on Software and Data Engineering, 10, 5, (2021), 161-168. DOI: https://doi.org/10.3745/KTSDE.2021.10.5.161.