Crack Detection on Concrete Surfaces Using Deep Encoder-Decoder Convolutional Neural Network: A Comparison Study Between U-Net and DeepLabV3

Maintenance of infrastructures is a crucial activity to ensure safety using crack detection methods on concrete structures. However, most practice of crack detection is carried out manually, which is unsafe, highly subjective, and time-consuming. Therefore, a more accurate and efficient system needs...

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Bibliographic Details
Published in:Journal of the Civil Engineering Forum Vol. 7; no. 3; pp. 323 - 334
Main Authors: Hadinata, Patrick Nicholas, Simanta, Djoni, Eddy, Liyanto, Nagai, Kohei
Format: Journal Article
Language:English
Japanese
Published: Universitas Gadjah Mada 31.08.2021
Subjects:
Civil Engineering
convolutional neural network
Convolutional Neural Network; U-Net; DeepLabV3+; Crack Detection; Maintenance of Infrastructures
crack detection
deeplabv3
Engineering (General). Civil engineering (General)
maintenance of infrastructures
TA1-2040
u-net
ISSN:2581-1037, 2549-5925
Online Access:Get full text
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Summary:Maintenance of infrastructures is a crucial activity to ensure safety using crack detection methods on concrete structures. However, most practice of crack detection is carried out manually, which is unsafe, highly subjective, and time-consuming. Therefore, a more accurate and efficient system needs to be implemented using artificial intelligence. Convolutional neural network (CNN), a subset of artificial intelligence, is used to detect cracks on concrete surfaces through semantic image segmentation. The purpose of this research is to compare the effectiveness of cutting-edge encoder-decoder architectures in detecting cracks on concrete surfaces using U-Net and DeepLabV3+ architectures with potential in biomedical, and sparse multiscale image segmentations, respectively. Neural networks were trained using cloud computing with a high-performance Graphics Processing Unit NVIDIA Tesla V100 and 27.4 GB of RAM. This study used internal and external data. Internal data consisted of simple cracks and were used as the training and validation data. Meanwhile, external data consisted of more complex cracks, which were used for further testing. Both architectures were compared based on four evaluation metrics in terms of accuracy, F1, precision, and recall. U-Net achieved segmentation accuracy = 96.57%, F1 = 87.55%, precision = 88.15%, and recall = 88.94%, while DeepLabV3+ achieved segmentation accuracy = 96.47%, F1 = 85.29%, precision = 92.07%, and recall = 81.84%. Experiment results (internal and external data) indicated that both architectures were accurate and effective in segmenting cracks. Additionally, U-Net and DeepLabV3+ exceeded the performance of previously tested architecture, namely FCN.
ISSN:2581-1037
2549-5925
DOI:10.22146/jcef.65288