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http://dx.doi.org/10.3745/KTSDE.2021.10.5.161

Deep Learning Models for Autonomous Crack Detection System  

Ji, HongGeun (성균관대학교 인공지능융합학과)
Kim, Jina (성균관대학교 인터랙션사이언스)
Hwang, Syjung (성균관대학교 인터랙션사이언스)
Kim, Dogun (성균관대학교 인공지능융합학과)
Park, Eunil (성균관대학교 인터랙션사이언스학과, 인공지능융합학과)
Kim, Young Seok (한국건설기술연구원 인프라안전연구본부)
Ryu, Seung Ki (한국건설기술연구원 차세대 인프라연구센터)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.5, 2021 , pp. 161-168 More about this Journal
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.
Keywords
Surface Inspection; Crack Detection; Computer Vision; Deep Learning;
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