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CCN: Pavement Crack Detection with Context Contrasted Net

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Neural Information Processing (ICONIP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13625))

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Abstract

Different from general object detection, there are three special characteristics in pavement crack detection: the tiny foreground proportion, the weak semanticity, and the local-whole similarity. To overcome the above challenges, we first construct Context Contrasted Network (CCN) to capture multi-scales features. By contrasting contextual features with local features, CCN contains the ability of local-whole discriminating. Then, we add an attention module in CCN to force the network to pay more attention to the foreground with small proportion instead of the background with large proportion, which improves the sensitivity of the model for pavement cracks. We achieve state-of-the-art results on two pavement crack datasets (i.e. CRACK500 and GRDDC), which demonstrates the effectiveness of our proposed method. Our source code will be opened later.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Nanjing University, Nanjing, China

    Yihuan Zhu, Sheng Zhang & Chengfeng Ruan

Authors
  1. Yihuan Zhu
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  2. Sheng Zhang
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  3. Chengfeng Ruan
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Corresponding author

Correspondence to Yihuan Zhu .

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Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

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Zhu, Y., Zhang, S., Ruan, C. (2023). CCN: Pavement Crack Detection with Context Contrasted Net. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13625. Springer, Cham. https://doi.org/10.1007/978-3-031-30111-7_8

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  • DOI: https://doi.org/10.1007/978-3-031-30111-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30110-0

  • Online ISBN: 978-3-031-30111-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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