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SP-CrackNet: serial–parallel network with boundary contrastive learning for real-time crack detection

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Abstract

Crack detectors based on deep learning have made tremendous progress compared to inefficient traditional inspections. However, missing thin local structures, blurred boundary segmentation and slow inference speed restrict the performance improvement of existing crack detectors. To this end, we propose a serial–parallel network (SP-CrackNet) for pixel-level real-time crack detection. Specifically, a serial–parallel feature extractor with global bottleneck blocks (GB Block) is proposed. Based on the serial–parallel structure, SPFE can enlarge the receiving field and capture the local information of thin cracks effectively, and the GB Block designed by us can ensure the continuity of the slender cracks is sensed with a lower computational cost. Moreover, a boundary contrastive learning scheme (BCL scheme) is designed to enhance the learning ability of SP-CrackNet for crack boundary features, thereby improving the segmentation accuracy of crack boundary regions. Extensive experiments on CFD and DeepCrack datasets show that SP-CrackNet outperforms the comparative methods while achieving real-time inference speed.

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The research has been used publicly available dataset.

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Acknowledgements

This work is supported by the Fundamental Research Funds for the Central Universities (Science and technology leading talent team project) (2022JBQY009), National Nature Science Foundation of China (51827813), National Key R &D Program “Transportation Infrastructure” “Reveal the list and take command” project (2022YFB2603302) and R &D Program of Beijing Municipal Education Commission (KJZD20191000402).

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Ying Xie, Hui Yin, Aixin Chong & Ying Yang

  2. Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing, 100044, China

    Ying Xie & Hui Yin

  3. Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University, Beijing, 100044, China

    Ying Xie & Hui Yin

  4. Key Laboratory of Beijing for Railway Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Aixin Chong & Ying Yang

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  1. Ying Xie
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  2. Hui Yin
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  3. Aixin Chong
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  4. Ying Yang
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Contributions

Xie Ying was responsible for all experiments and main manuscript text. Yin Hui was in charge of writing main manuscript text. Chong Aixin was responsible for main manuscript text and figures. Yang Ying wrote the main manuscript text. All authors reviewed the manuscript.

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Correspondence to Hui Yin.

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Xie, Y., Yin, H., Chong, A. et al. SP-CrackNet: serial–parallel network with boundary contrastive learning for real-time crack detection. SIViP 18, 3265–3274 (2024). https://doi.org/10.1007/s11760-023-02988-z

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  • DOI: https://doi.org/10.1007/s11760-023-02988-z

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