Detecting cracks in concrete structures with the baseline model of the visual characteristics of images

Liu, Yang; Gao, Mei

doi:10.1111/mice.12874

“…Mainly two targets were discussed: cracks on concrete structure surfaces and asphalt pavement road surfaces. Cracks on concrete surfaces are relatively thin and have complicated structures (Chun et al [2021], Dung & Anh [2019], Fujita & Hamamoto [2011], Kim & Cho [2019], Liu & Gao [2022], Nishikawa et al [2012]). Usually, thickness is smaller than 1 mm.…”

Section: Previous Researchmentioning

confidence: 99%

Quantitative Road Crack Evaluation by a U-Net Architecture using Smartphone Images and Lidar Data

Yamaguchi¹,

Mizutani²

2022

Preprint

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<p> Road cracks are an important concern of administrators. Visual inspection is labor-intensive and subjective, while previous algorithms detecting cracks from optical camera images were not accurate. Furthermore, the actual length and thicknesses of a crack cannot be estimated only from images. Light Detection and Ranging (Lidar) is a standard feature introduced on the latest smartphones. In this research, for completely automatic, accurate and quantitative road crack evaluation using smartphones, an up-to-date segmentation technique, U-Net with morphology transform adopting data augmentation was proposed. Lidar 3D point cloud data of smartphones is linked to color data obtained from cameras. By registering images to Lidar data, geometrical relationships were estimated to calculate the length and thicknesses. The proposed algorithm was validated by a standard database of road cracks and dataset constructed by the authors, showing 95% length accuracy and 0.98 coefficient of determination for thickness estimation irrespective of various crack shapes and asphalt pavement color patterns. </p>

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“…Mainly two targets were discussed: cracks on concrete structure surfaces and asphalt pavement road surfaces. Cracks on concrete surfaces are relatively thin and have complicated structures (Chun et al [2021], Dung & Anh [2019], Fujita & Hamamoto [2011], Kim & Cho [2019], Liu & Gao [2022], Nishikawa et al [2012]). Usually, thickness is smaller than 1 mm.…”

Section: Previous Researchmentioning

confidence: 99%

Quantitative Road Crack Evaluation by a U-Net Architecture using Smartphone Images and Lidar Data

Yamaguchi¹,

Mizutani²

2022

Preprint

4

0

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<p> Road cracks are an important concern of administrators. Visual inspection is labor-intensive and subjective, while previous algorithms detecting cracks from optical camera images were not accurate. Furthermore, the actual length and thicknesses of a crack cannot be estimated only from images. Light Detection and Ranging (Lidar) is a standard feature introduced on the latest smartphones. In this research, for completely automatic, accurate and quantitative road crack evaluation using smartphones, an up-to-date segmentation technique, U-Net with morphology transform adopting data augmentation was proposed. Lidar 3D point cloud data of smartphones is linked to color data obtained from cameras. By registering images to Lidar data, geometrical relationships were estimated to calculate the length and thicknesses. The proposed algorithm was validated by a standard database of road cracks and dataset constructed by the authors, showing 95% length accuracy and 0.98 coefficient of determination for thickness estimation irrespective of various crack shapes and asphalt pavement color patterns. </p>

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“…As a concealed project buried in underground geotechnical medium, various types of defects (e.g., crack, leakage, rebar‐exposed, and cavity) are inevitably observed in tunnel lining under the action of complex environment. Among them, crack is the most common defect (Liu & Gao, 2022; Zhao et al., 2021; Gong et al., 2023), which is mainly caused by eccentric load, groundwater, long‐term differential settlement, temperature change, internal vehicle load, and adjacent excavations. According to Technical Specifications of Maintenance for Highway Tunnel (JTGH12‐2015) in China, the allowable lining crack is required to limit within 0.2 mm.…”

Section: Introductionmentioning

confidence: 99%

Hybrid semantic segmentation for tunnel lining cracks based on Swin Transformer and convolutional neural network

Zhou

¹

,

Zhang

²

,

Gong

³

2023

Computer aided Civil Eng

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In the field of tunnel lining crack identification, the semantic segmentation algorithms based on convolution neural network (CNN) are extensively used. Owing to the inherent locality of CNN, these algorithms cannot make full use of context semantic information, resulting in difficulty in capturing the global features of crack. Transformer‐based networks can capture global semantic information, but this method also has the deficiencies of strong data dependence and easy loss of local features. In this paper, a hybrid semantic segmentation algorithm for tunnel lining crack, named SCDeepLab, is proposed by fusing Swin Transformer and CNN in the encoding and decoding framework of DeepLabv3+ to address the above issues. In SCDeepLab, a joint backbone network is introduced with CNN‐based Inverse Residual Block and Swin Transformer Block. The former is used to extract the local detailed information of the crack to generate the shallow feature layer, whereas the latter is used to extract the global semantic information to obtain the deep feature layer. In addition, Efficient Channel Attention enhanced Feature Fusion Module is proposed to fuse the shallow and deep features to combine the advantages of the two types of features. Furthermore, the strategy of transfer learning is adopted to solve the data dependency of Swin Transformer. The results show that the mean intersection over union (mIoU) and mean pixel accuracy (mPA) of SCDeepLab on the data sets constructed in this paper are 77.41% and 84.42%, respectively, which have higher segmentation accuracy than previous CNN‐based and transformer‐based semantic segmentation algorithms.

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Detecting cracks in concrete structures with the baseline model of the visual characteristics of images

Cited by 15 publications

References 69 publications

Quantitative Road Crack Evaluation by a U-Net Architecture using Smartphone Images and Lidar Data

Quantitative Road Crack Evaluation by a U-Net Architecture using Smartphone Images and Lidar Data

Quantitative Road Crack Evaluation by a U-Net Architecture using Smartphone Images and Lidar Data

Hybrid semantic segmentation for tunnel lining cracks based on Swin Transformer and convolutional neural network

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