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Link to original content: https://doi.org/10.1007/s11042-024-18568-3
Toward enhancing concrete crack segmentation accuracy under complex scenarios: a novel modified U-Net network | Multimedia Tools and Applications Skip to main content
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Toward enhancing concrete crack segmentation accuracy under complex scenarios: a novel modified U-Net network

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Abstract

Convolutional neural networks (CNNs) have demonstrated promising accuracy in segmenting concrete cracks under controlled conditions. However, these existing methods usually are challenging in addressing multi-scale patterns or complex scenarios. To address this pervasive issue, a modified U-Net network was established in the current study. The proposed model, namely U-Net-ASPP-CBAM, integrates the convolutional block attention module after a convolution operator to selectively focus on the crack information while disregarding irrelevant background details during feature extraction. Furthermore, U-Net-ASPP-CBAM network replaces a pooling layer using the atrous spatial pyramid pooling module to explore and fuse features across multiple scales, enhancing information capture for the segmentation of small objects. The performance of the proposed model has been validated by a self-built dataset comprising crack images with diverse complex backgrounds. And the segmentation effectiveness is assessed through evaluation indices, including precision, recall, F1 score, pixel accuracy, and mean intersection over union. The results show the proposed U-Net-ASPP-CBAM model outperforms other segmentation models.

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Acknowledgements

The authors gratefully appreciate the financial support of the Nanjing International joint research and development project of China (2022SX00001057) and the National Natural Science Foundation of China (52078122) are greatly appreciated.

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Authors and Affiliations

  1. School of Civil Engineering, Southeast University, Nanjing, 211189, China

    Feng Qu, Bokun Wang, Qing Zhu & Caiqian Yang

  2. Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing, 211189, China

    Feng Qu, Bokun Wang, Qing Zhu & Caiqian Yang

  3. School of Civil Engineering, Xiangtan University, Xiangtan, 411105, China

    Fu Xu

  4. Jiangsu Expressway Engineering Maintenance Co., Ltd, Huai’an, 223000, China

    Yaojing Chen

Authors
  1. Feng Qu
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  4. Fu Xu
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Contributions

Feng Qu: Writing-original draft, Investigation, Formal analysis. Bokun Wang: Investigation. Qing Zhu: Investigation. Fu Xu: Supervision, Conceptualization. Yaojing Chen: Supervision, Conceptualization. Caiqian Yang: Conceptualization, Supervision, Writing-review & editing.

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Correspondence to Caiqian Yang.

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Qu, F., Wang, B., Zhu, Q. et al. Toward enhancing concrete crack segmentation accuracy under complex scenarios: a novel modified U-Net network. Multimed Tools Appl 83, 76935–76952 (2024). https://doi.org/10.1007/s11042-024-18568-3

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