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A platform for research: civil engineering, architecture and urbanism
Image-based concrete crack detection in tunnels using deep fully convolutional networks
https://orcid.org/0000-0002-6090-0161
Highlights Deep learning-based automatic segmentation of concrete cracks in tunnels. A new end-to-end crack segmentation method based on fully convolutional networks. More efficiency and higher accuracy than the conventional and other deep learning-based crack segmentation methods. Use of dilated convolution, spatial pyramid pooling, skip connections, and an optimized loss function.
Abstract Automatic detection and segmentation of concrete cracks in tunnels remains a high-priority task for civil engineers. Image-based crack segmentation is an effective method for crack detection in tunnels. With the development of deep learning techniques, especially the development of image segmentation based on convolutional neural networks, new opportunities have been brought to crack detection. In this study, an improved deep fully convolutional neural network, named as CrackSegNet, is proposed to conduct dense pixel-wise crack segmentation. The proposed network consists of a backbone network, dilated convolution, spatial pyramid pooling, and skip connection modules. These modules can be used for efficient multiscale feature extraction, aggregation, and resolution reconstruction which greatly enhance the overall crack segmentation ability of the network. Compared to the conventional image processing and other deep learning-based crack segmentation methods, the proposed network shows significantly higher accuracy and generalization, making tunnel inspection and monitoring highly efficient, low cost, and eventually automatable.
Image-based concrete crack detection in tunnels using deep fully convolutional networks
https://orcid.org/0000-0002-6090-0161
Highlights Deep learning-based automatic segmentation of concrete cracks in tunnels. A new end-to-end crack segmentation method based on fully convolutional networks. More efficiency and higher accuracy than the conventional and other deep learning-based crack segmentation methods. Use of dilated convolution, spatial pyramid pooling, skip connections, and an optimized loss function.
Abstract Automatic detection and segmentation of concrete cracks in tunnels remains a high-priority task for civil engineers. Image-based crack segmentation is an effective method for crack detection in tunnels. With the development of deep learning techniques, especially the development of image segmentation based on convolutional neural networks, new opportunities have been brought to crack detection. In this study, an improved deep fully convolutional neural network, named as CrackSegNet, is proposed to conduct dense pixel-wise crack segmentation. The proposed network consists of a backbone network, dilated convolution, spatial pyramid pooling, and skip connection modules. These modules can be used for efficient multiscale feature extraction, aggregation, and resolution reconstruction which greatly enhance the overall crack segmentation ability of the network. Compared to the conventional image processing and other deep learning-based crack segmentation methods, the proposed network shows significantly higher accuracy and generalization, making tunnel inspection and monitoring highly efficient, low cost, and eventually automatable.
Image-based concrete crack detection in tunnels using deep fully convolutional networks
https://orcid.org/0000-0002-6090-0161
Highlights Deep learning-based automatic segmentation of concrete cracks in tunnels. A new end-to-end crack segmentation method based on fully convolutional networks. More efficiency and higher accuracy than the conventional and other deep learning-based crack segmentation methods. Use of dilated convolution, spatial pyramid pooling, skip connections, and an optimized loss function.
Abstract Automatic detection and segmentation of concrete cracks in tunnels remains a high-priority task for civil engineers. Image-based crack segmentation is an effective method for crack detection in tunnels. With the development of deep learning techniques, especially the development of image segmentation based on convolutional neural networks, new opportunities have been brought to crack detection. In this study, an improved deep fully convolutional neural network, named as CrackSegNet, is proposed to conduct dense pixel-wise crack segmentation. The proposed network consists of a backbone network, dilated convolution, spatial pyramid pooling, and skip connection modules. These modules can be used for efficient multiscale feature extraction, aggregation, and resolution reconstruction which greatly enhance the overall crack segmentation ability of the network. Compared to the conventional image processing and other deep learning-based crack segmentation methods, the proposed network shows significantly higher accuracy and generalization, making tunnel inspection and monitoring highly efficient, low cost, and eventually automatable.
Image-based concrete crack detection in tunnels using deep fully convolutional networks
Ren, Yupeng (author) / Huang, Jisheng (author) / Hong, Zhiyou (author) / Lu, Wei (author) / Yin, Jun (author) / Zou, Lejun (author) / Shen, Xiaohua (author)
2019-10-23
Article (Journal)
Electronic Resource
English
SHM , structural health monitoring , CNN , convolutional neural network , FCN , fully convolutional network , PSPNet , Pyramid Scene Parsing Network , SPP , spatial pyramid pooling , ReLU , Rectified Linear Unit , PA , pixel accuracy , IoU , Intersection over union , Concrete , Crack detection , Deep learning , Convolutional neural network , Pixel-wise segmentation , Structural health monitoring
Structural crack detection using deep learning–based fully convolutional networks
| SAGE Publications | 2019
Autonomous concrete crack detection using deep fully convolutional neural network
| British Library Online Contents | 2019