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A platform for research: civil engineering, architecture and urbanism
Autonomous concrete crack detection using deep fully convolutional neural network
Abstract Crack detection is a critical task in monitoring and inspection of civil engineering structures. Image classification and bounding box approaches have been proposed in existing vision-based automated concrete crack detection methods using deep convolutional neural networks. The current study proposes a crack detection method based on deep fully convolutional network (FCN) for semantic segmentation on concrete crack images. Performance of three different pre-trained network architectures, which serves as the FCN encoder's backbone, is evaluated for image classification on a public concrete crack dataset of 40,000 227 × 227 pixel images. Subsequently, the whole encoder-decoder FCN network with the VGG16-based encoder is trained end-to-end on a subset of 500 annotated 227 × 227-pixel crack-labeled images for semantic segmentation. The FCN network achieves about 90% in average precision. Images extracted from a video of a cyclic loading test on a concrete specimen are used to validate the proposed method for concrete crack detection. It was found that cracks are reasonably detected and crack density is also accurately evaluated.
Highlights Crack classifiers built on pre-trained networks achieve at least 97.8% in accuracy. Semantic segmentation method produces about 90% in average precision. Semantic segmentation method can capture crack size reasonably.
Autonomous concrete crack detection using deep fully convolutional neural network
Abstract Crack detection is a critical task in monitoring and inspection of civil engineering structures. Image classification and bounding box approaches have been proposed in existing vision-based automated concrete crack detection methods using deep convolutional neural networks. The current study proposes a crack detection method based on deep fully convolutional network (FCN) for semantic segmentation on concrete crack images. Performance of three different pre-trained network architectures, which serves as the FCN encoder's backbone, is evaluated for image classification on a public concrete crack dataset of 40,000 227 × 227 pixel images. Subsequently, the whole encoder-decoder FCN network with the VGG16-based encoder is trained end-to-end on a subset of 500 annotated 227 × 227-pixel crack-labeled images for semantic segmentation. The FCN network achieves about 90% in average precision. Images extracted from a video of a cyclic loading test on a concrete specimen are used to validate the proposed method for concrete crack detection. It was found that cracks are reasonably detected and crack density is also accurately evaluated.
Highlights Crack classifiers built on pre-trained networks achieve at least 97.8% in accuracy. Semantic segmentation method produces about 90% in average precision. Semantic segmentation method can capture crack size reasonably.
Autonomous concrete crack detection using deep fully convolutional neural network
Dung, Cao Vu (author) / Anh, Le Duc (author)
Automation in Construction ; 99 ; 52-58
2018-11-29
7 pages
Article (Journal)
Electronic Resource
English
Autonomous concrete crack detection using deep fully convolutional neural network
| British Library Online Contents | 2019