A novel U‐shaped encoder–decoder network with attention mechanism for detection and evaluation of road cracks at pixel level

Chen, Jun; He, Ye

doi:10.1111/mice.12826

Cited by 47 publications

(34 citation statements)

References 46 publications

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“…32 In crack detection, there have been attempts to combine the attention mechanism with available networks to improve detection efficiency. [33][34][35] For example, Pan et al 36 modified the backbone of DANet from ResNet101 to VGG19, namely SCHNet, and added a new attention mechanism named feature pyramid attention to improve crack detection accuracy. The results demonstrated that three attention mechanisms can increase MIoU by 10.88% than the baseline model.…”

Section: Introductionmentioning

confidence: 99%

A deep learning semantic segmentation network with attention mechanism for concrete crack detection

Hang

et al. 2023

Structural Health Monitoring

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In this research, an attention-based feature fusion network (AFFNet), with a backbone residual network (ResNet101) enhanced with two attention mechanism modules, is proposed for automatic pixel-level detection of concrete crack. In particular, the inclusion of attention mechanism modules, for example, the vertical and horizontal compression attention module (VH-CAM) and the efficient channel attention upsample module (ECAUM), is to enable selective concentration on the crack feature. The VH-CAM generates a feature map integrating pixel-level information in vertical and horizontal directions. The ECAUM applied on each decoder layer combines efficient channel attention (ECA) and feature fusion, which can provide rich contextual information as guidance to help low-level features recover crack localization. The proposed model is evaluated on the test dataset and the results reach 84.49% for mean intersection over union (MIoU). Comparison with other state-of-the-art models proves high efficiency and accuracy of the proposed method.

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Section: Introductionmentioning

confidence: 99%

A deep learning semantic segmentation network with attention mechanism for concrete crack detection

Hang

et al. 2023

Structural Health Monitoring

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“…(2019) to accomplish pixel‐level detection of road cracks using black‐box images. In addition, many other deep learning solutions for pavement crack detection were also prompted with various successes in the last 5 years, including the DeepCrack (Zou et al., 2019), FPHBN (F. Yang et al., 2020), two‐step CNN (Liu et al., 2020), EDNet (Tang et al., 2021), U‐shaped encoder–decoder network (J. Chen & He, 2022), and so forth.…”

Section: Introductionmentioning

confidence: 99%

Intelligent pixel‐level detection of multiple distresses and surface design features on asphalt pavements

Zhang

Wang

Liu

et al. 2022

Computer aided Civil Eng

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Simultaneous pixel-level detection of multiple distresses and surface design features on complex asphalt pavements is a critical challenge in intelligent pavement survey. This paper proposes a deep-learning model named ShuttleNet to provide an efficient solution for this challenge by implementing robust semantic segmentation on asphalt pavements. The proposed ShuttleNet aims at repeating the encoding-decoding round freely or even endlessly such that the contexts at different resolution levels can be learned and integrated many times for enhanced latent representations. Additionally, a new and efficient connection method called memory connection is also proposed in the paper and deployed in the ShuttleNet model to provide shortcut connections between successive encoding-decoding rounds. The proposed memory connection can partially or entirely carry the decoded information at different resolution levels into the next encoding-decoding round. Pairing 3D pavement images with 2D pavement images, the proposed ShuttleNet model is applied to detect multiple distresses and surface design features on asphalt pavements simultaneously, including pavement cracks, potholes, sealed cracks, patches, markings, expansion joints, and the pavement background. Experimental results demonstrate that the mean F-measure and mean intersection-over-union attained by the recommended architectural variation of the proposed ShuttleNet model on 1500 testing image pairs are 92.54% and 0.8657 respectively. According to the performance comparisons using both private and public datasets, the proposed ShuttleNet model can yield a noticeably higher detection accuracy, compared with four state-of-the-art models for semantic segmentation.

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“…Zhang et al, 2017), UNet (J. Chen & He, 2022), recurrent neural network (RNN; A. Zhang et al, 2018), and DeepLab (Meng et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…The current research on crack edge detection based on deep learning is mainly to improve the methods based on a fully convolutional network (FCN; Dung & Anh, 2019; Li et al., 2019; Ye et al., 2019), a self‐attention mechanism (Y. Pan et al., 2020; Song et al., 2019), CNN (Fei et al., 2019; S.‐Y. Kong et al., 2021; Ni et al., 2019; A. Zhang et al., 2017), UNet (J. Chen & He, 2022), recurrent neural network (RNN; A. Zhang et al., 2018), and DeepLab (Meng et al., 2020). Besides, some researchers utilize multi‐stage crack detection and achieve high‐precision crack edge detection by combining the classification algorithm with the segmentation algorithm (J. Liu et al., 2020; Meng et al., 2022).…”

Section: Introductionmentioning

confidence: 99%

Real‐time automatic crack detection method based on drone

Meng

Gao

Zhou

et al. 2022

Computer aided Civil Eng

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Real-time automated drone-based crack detection can be used for efficient building damage assessment. This paper proposes an automated real-time crack detection method based on a drone. Using a lightweight classification algorithm, a lightweight segmentation algorithm, a high-precision segmentation algorithm, and a crack width measurement algorithm, the cracks are classified, roughly segmented, finely segmented, and the maximum width is extracted. A crack information-assisted drone flight automatic control algorithm for automatic crack detection guides the drone toward the crack position. The effectiveness of the crack detection algorithm and the crack information-assisted drone flight automatic control algorithm was tested on two different datasets, a two-story building, and a 16-m-high shaking table test building. The results show that crack detection can be finished in real-time during the flight. Using the proposed method can significantly improve the MIoU of crack edge detection and the accuracy of maximum crack width measurement under the non-ideal shooting conditions of the actual inspection situation by automatically approaching the vicinity of the crack.

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A novel U‐shaped encoder–decoder network with attention mechanism for detection and evaluation of road cracks at pixel level

Cited by 47 publications

References 46 publications

A deep learning semantic segmentation network with attention mechanism for concrete crack detection

A deep learning semantic segmentation network with attention mechanism for concrete crack detection

Intelligent pixel‐level detection of multiple distresses and surface design features on asphalt pavements

Real‐time automatic crack detection method based on drone

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