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A platform for research: civil engineering, architecture and urbanism
Image-based Concrete Cracks Identification under Complex Background with Lightweight Convolutional Neural Network
https://orcid.org/https://orcid.org/0009-0001-3171-5581
https://orcid.org/https://orcid.org/0000-0001-5550-2794
https://orcid.org/https://orcid.org/0009-0000-8136-8840
https://orcid.org/https://orcid.org/0000-0001-6148-6401
https://orcid.org/https://orcid.org/0009-0007-0463-5440
https://orcid.org/https://orcid.org/0009-0003-8615-9855
https://orcid.org/https://orcid.org/0009-0009-7336-6402
Cracks are important indexes to evaluate the health status of concrete structures. To accurately and automatically identify the cracks of concrete structures, and solve the time-consuming and labor-intensive limits of manual detection methods, this paper proposed an image-based concrete cracks identification method based on a lightweight Convolutional Neural Network, which includes three modules: crack classification, semantic segmentation and quantitative calculation of crack geometric size. Firstly, the S_MobileNet was used to classify cracks, exclude irrelevant regions, and reduce the interference of non-crack images; Secondly, the optimized method SM-UNet based on the U-Net network was employed to segment the detected crack image at the pixel level; Finally, based on the results of crack semantic segmentation, image post-processing technology was used to realize the quantitative calculation of crack geometric size parameters, which provides a basis for crack damage assessment of concrete structures. The experimental results show that this study provides a solution for the automatic detection of crack images and high-precision measurement of crack size, which has an important value in scientific research and engineering application.
Image-based Concrete Cracks Identification under Complex Background with Lightweight Convolutional Neural Network
https://orcid.org/https://orcid.org/0009-0001-3171-5581
https://orcid.org/https://orcid.org/0000-0001-5550-2794
https://orcid.org/https://orcid.org/0009-0000-8136-8840
https://orcid.org/https://orcid.org/0000-0001-6148-6401
https://orcid.org/https://orcid.org/0009-0007-0463-5440
https://orcid.org/https://orcid.org/0009-0003-8615-9855
https://orcid.org/https://orcid.org/0009-0009-7336-6402
Cracks are important indexes to evaluate the health status of concrete structures. To accurately and automatically identify the cracks of concrete structures, and solve the time-consuming and labor-intensive limits of manual detection methods, this paper proposed an image-based concrete cracks identification method based on a lightweight Convolutional Neural Network, which includes three modules: crack classification, semantic segmentation and quantitative calculation of crack geometric size. Firstly, the S_MobileNet was used to classify cracks, exclude irrelevant regions, and reduce the interference of non-crack images; Secondly, the optimized method SM-UNet based on the U-Net network was employed to segment the detected crack image at the pixel level; Finally, based on the results of crack semantic segmentation, image post-processing technology was used to realize the quantitative calculation of crack geometric size parameters, which provides a basis for crack damage assessment of concrete structures. The experimental results show that this study provides a solution for the automatic detection of crack images and high-precision measurement of crack size, which has an important value in scientific research and engineering application.
Image-based Concrete Cracks Identification under Complex Background with Lightweight Convolutional Neural Network
https://orcid.org/https://orcid.org/0009-0001-3171-5581
https://orcid.org/https://orcid.org/0000-0001-5550-2794
https://orcid.org/https://orcid.org/0009-0000-8136-8840
https://orcid.org/https://orcid.org/0000-0001-6148-6401
https://orcid.org/https://orcid.org/0009-0007-0463-5440
https://orcid.org/https://orcid.org/0009-0003-8615-9855
https://orcid.org/https://orcid.org/0009-0009-7336-6402
Cracks are important indexes to evaluate the health status of concrete structures. To accurately and automatically identify the cracks of concrete structures, and solve the time-consuming and labor-intensive limits of manual detection methods, this paper proposed an image-based concrete cracks identification method based on a lightweight Convolutional Neural Network, which includes three modules: crack classification, semantic segmentation and quantitative calculation of crack geometric size. Firstly, the S_MobileNet was used to classify cracks, exclude irrelevant regions, and reduce the interference of non-crack images; Secondly, the optimized method SM-UNet based on the U-Net network was employed to segment the detected crack image at the pixel level; Finally, based on the results of crack semantic segmentation, image post-processing technology was used to realize the quantitative calculation of crack geometric size parameters, which provides a basis for crack damage assessment of concrete structures. The experimental results show that this study provides a solution for the automatic detection of crack images and high-precision measurement of crack size, which has an important value in scientific research and engineering application.
Image-based Concrete Cracks Identification under Complex Background with Lightweight Convolutional Neural Network
KSCE J Civ Eng
Meng, Qingcheng (author) / Hu, Lei (author) / Wan, Da (author) / Li, Mingjian (author) / Wu, Haojie (author) / Qi, Xin (author) / Tian, Yongding (author)
KSCE Journal of Civil Engineering ; 27 ; 5231-5242
2023-12-01
12 pages
Article (Journal)
Electronic Resource
English
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