Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks

Constant monitoring of road surfaces helps to show the urgency of deterioration or problems in the road construction and to improve the safety level of the road surface. Conditional generative adversarial networks (cGAN) are a powerful tool to generate or transform the images used for crack detectio...

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Autores principales:	Anastasiia Kyslytsyna, Kewen Xia, Artem Kislitsyn, Isselmou Abd El Kader, Youxi Wu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	conditional generative adversarial networks attention gate road crack detection dashboard images dataset Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/b769ecb2a90141fd8d3b9ae403e42a4e
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id	oai:doaj.org-article:b769ecb2a90141fd8d3b9ae403e42a4e
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spelling	oai:doaj.org-article:b769ecb2a90141fd8d3b9ae403e42a4e2021-11-11T19:19:38ZRoad Surface Crack Detection Method Based on Conditional Generative Adversarial Networks10.3390/s212174051424-8220https://doaj.org/article/b769ecb2a90141fd8d3b9ae403e42a4e2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7405https://doaj.org/toc/1424-8220Constant monitoring of road surfaces helps to show the urgency of deterioration or problems in the road construction and to improve the safety level of the road surface. Conditional generative adversarial networks (cGAN) are a powerful tool to generate or transform the images used for crack detection. The advantage of this method is the highly accurate results in vector-based images, which are convenient for mathematical analysis of the detected cracks at a later time. However, images taken under established parameters are different from images in real-world contexts. Another potential problem of cGAN is that it is difficult to detect the shape of an object when the resulting accuracy is low, which can seriously affect any further mathematical analysis of the detected crack. To tackle this issue, this paper proposes a method called improved cGAN with attention gate (ICGA) for roadway surface crack detection. To obtain a more accurate shape of the detected target object, ICGA establishes a multi-level model with independent stages. In the first stage, everything except the road is treated as noise and removed from the image. These images are stored in a new dataset. In the second stage, ICGA determines the cracks. Therefore, ICGA focuses on the redistribution of cracks, not the auxiliary elements in the image. ICGA adds two attention gates to a U-net architecture and improves the segmentation capacities of the generator in pix2pix. Extensive experimental results on dashboard camera images of the Unsupervised Llamas dataset show that our method has better performance than other state-of-the-art methods.Anastasiia KyslytsynaKewen XiaArtem KislitsynIsselmou Abd El KaderYouxi WuMDPI AGarticleconditional generative adversarial networksattention gateroad crack detectiondashboard images datasetChemical technologyTP1-1185ENSensors, Vol 21, Iss 7405, p 7405 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	conditional generative adversarial networks attention gate road crack detection dashboard images dataset Chemical technology TP1-1185
spellingShingle	conditional generative adversarial networks attention gate road crack detection dashboard images dataset Chemical technology TP1-1185 Anastasiia Kyslytsyna Kewen Xia Artem Kislitsyn Isselmou Abd El Kader Youxi Wu Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks
description	Constant monitoring of road surfaces helps to show the urgency of deterioration or problems in the road construction and to improve the safety level of the road surface. Conditional generative adversarial networks (cGAN) are a powerful tool to generate or transform the images used for crack detection. The advantage of this method is the highly accurate results in vector-based images, which are convenient for mathematical analysis of the detected cracks at a later time. However, images taken under established parameters are different from images in real-world contexts. Another potential problem of cGAN is that it is difficult to detect the shape of an object when the resulting accuracy is low, which can seriously affect any further mathematical analysis of the detected crack. To tackle this issue, this paper proposes a method called improved cGAN with attention gate (ICGA) for roadway surface crack detection. To obtain a more accurate shape of the detected target object, ICGA establishes a multi-level model with independent stages. In the first stage, everything except the road is treated as noise and removed from the image. These images are stored in a new dataset. In the second stage, ICGA determines the cracks. Therefore, ICGA focuses on the redistribution of cracks, not the auxiliary elements in the image. ICGA adds two attention gates to a U-net architecture and improves the segmentation capacities of the generator in pix2pix. Extensive experimental results on dashboard camera images of the Unsupervised Llamas dataset show that our method has better performance than other state-of-the-art methods.
format	article
author	Anastasiia Kyslytsyna Kewen Xia Artem Kislitsyn Isselmou Abd El Kader Youxi Wu
author_facet	Anastasiia Kyslytsyna Kewen Xia Artem Kislitsyn Isselmou Abd El Kader Youxi Wu
author_sort	Anastasiia Kyslytsyna
title	Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks
title_short	Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks
title_full	Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks
title_fullStr	Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks
title_full_unstemmed	Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks
title_sort	road surface crack detection method based on conditional generative adversarial networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b769ecb2a90141fd8d3b9ae403e42a4e
work_keys_str_mv	AT anastasiiakyslytsyna roadsurfacecrackdetectionmethodbasedonconditionalgenerativeadversarialnetworks AT kewenxia roadsurfacecrackdetectionmethodbasedonconditionalgenerativeadversarialnetworks AT artemkislitsyn roadsurfacecrackdetectionmethodbasedonconditionalgenerativeadversarialnetworks AT isselmouabdelkader roadsurfacecrackdetectionmethodbasedonconditionalgenerativeadversarialnetworks AT youxiwu roadsurfacecrackdetectionmethodbasedonconditionalgenerativeadversarialnetworks
_version_	1718431546993541120

Road Surface Crack Detection Method Based on Conditional Generative Adversarial Networks

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