research-article

CASA-Net: A Context-Aware Correlation Convolutional Network for Scale-Adaptive Crack Detection

Authors:

Guoren WangAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 67 - 76

https://doi.org/10.1145/3511808.3557252

Published: 17 October 2022 Publication History

Abstract

Surface cracks in infrastructure are a key indicator of structural safety and degradation. Visual-based crack detection is a critical task for the enormous application demands of infrastructure industries. Convolution operations have been widely deployed due to the strong feature learning abilities. However, global feature dependencies of multi-scale cracks are ignored due to the limited receptive field.In addition, the detection of cracks with low contrast suffers a serious performance loss.Therefore, to address the scale-adaptive crack detection problem, we propose a context-aware correlation convolutional network for scale-adaptive crack detection named CASA-Net. CASA-Net is capable of extracting multi-scale crack features for distinguishing between cracks and surface backgrounds, and evaluating feature correlations to capture global contexts. CASA-Net is composed of the multi-scale distinguishing feature extraction (MDFE) module and the context-aware feature correlation (CAFC) module. Specifically, the MDFE module consists of multiple cascaded convolutional layers and distinguishing feature extraction layers (DFLayers). The CAFC module consists of a mapping block and cascaded correlators to capture the context-aware features for long-range interactions. The performance of CASA-Net is evaluated on a benchmark crack dataset. The experimental results indicate that CASA-Net outperforms rival methods by achieving an F1-Score of 0.65 and an AP50 of 63.9%.

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Cited By

Yoo JShtaiwi RYasin MTrivedi DAlfarrarjeh AJabal AKim S(2024)Towards Real-world Deployment of Deep Learning Solutions for Global Road Damage Detection and Classification2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825129(8485-8494)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825129
Feng XYang CHe BYao ZHu LZhang WKong RZhao JLiu ZBi X(2024)Artificial intelligence technology in rock mechanics and rock engineeringDeep Resources Engineering10.1016/j.deepre.2024.1000081:2(100008)Online publication date: Jun-2024
https://doi.org/10.1016/j.deepre.2024.100008
Hu LFeng XYao ZZhang WNiu WBi XFeng GXiao Y(2023)Rockburst time warning method with blasting cycle as the unit based on microseismic information time series: a case studyBulletin of Engineering Geology and the Environment10.1007/s10064-023-03141-382:4Online publication date: 15-Mar-2023
https://doi.org/10.1007/s10064-023-03141-3

Index Terms

CASA-Net: A Context-Aware Correlation Convolutional Network for Scale-Adaptive Crack Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

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Published: 17 October 2022

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October 17 - 21, 2022

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Cited By

Yoo JShtaiwi RYasin MTrivedi DAlfarrarjeh AJabal AKim S(2024)Towards Real-world Deployment of Deep Learning Solutions for Global Road Damage Detection and Classification2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825129(8485-8494)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825129
Feng XYang CHe BYao ZHu LZhang WKong RZhao JLiu ZBi X(2024)Artificial intelligence technology in rock mechanics and rock engineeringDeep Resources Engineering10.1016/j.deepre.2024.1000081:2(100008)Online publication date: Jun-2024
https://doi.org/10.1016/j.deepre.2024.100008
Hu LFeng XYao ZZhang WNiu WBi XFeng GXiao Y(2023)Rockburst time warning method with blasting cycle as the unit based on microseismic information time series: a case studyBulletin of Engineering Geology and the Environment10.1007/s10064-023-03141-382:4Online publication date: 15-Mar-2023
https://doi.org/10.1007/s10064-023-03141-3

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