Autonomous concrete crack detection using deep fully convolutional neural network

By Cao Vu Dung, Le Duc Anh. Automation in Construction, 2019

1,058 citations recorded by the source corpus.

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.

Subfields: Civil and Structural Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/c1f277de5326780c3a37eac4424338c7bb0551eb

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In Civil and Structural Engineering

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Citations in the corpus

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review (2020, methodology): “Dung and Anh [33] Concrete crack detection, segmentation, and density evaluation A public dataset of 40,000 concrete crack 227 × 227 pixel images”
Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning (2021, methodology): “Another showcase for U-net was presented by Liu et al. [30] for concrete crack detection, where the U-net performed better than the other FCN methods [23,24] while being trained on significantly smaller training sets.”
SDDNet: Real-Time Crack Segmentation (2020, methodology): “Crack segmentation methods that implement DL have been developed in the recent research [19], [20], [21], [22], [23].”
Automated pavement crack detection and segmentation based on two‐step convolutional neural network (2020, methodology): “Dung and Anh (2019) proposed FIGURE 2 Flowchart of proposed two-step method a semantic crack segmentation method using deep fully convolutional network.”
Encoder–decoder network for pixel‐level road crack detection in black‐box images (2019, methodology): “Dung (2019) proposed a method for concrete crack detection using a whole encoder–decoder network with the VGG16-based encoder.”