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Towards Pavement Crack Detection and Classification Based on Machine Leaning Methods

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Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) (ICAUS 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 861))

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Abstract

Pavement cracks detection and classification is essential for assessing the highway condition. Researchers have studied many valuable machine learning algorithms to solve pavement cracks detection and classification problems. This paper indicates the problems and challenges that faced by researchers, and summarizes and analyzes the development of pavement cracks detection and classification. This paper discusses the principle and research status of crack detection method based on traditional machine learning method, and also discusses the research status and advantages of crack identification method based on deep learning methods. Finally, the possible trends and opportunities are discussed for further research.

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Author information

Authors and Affiliations

  1. School of Automation, Northwestern Polytechnical University, Xi’an, China

    Yuheng He & Mei Wu

  2. Highway Bureau of Shaanxi Province, Xi’an, China

    Min Sun & Lu Wang

Authors
  1. Yuheng He
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  2. Mei Wu
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  3. Min Sun
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  4. Lu Wang
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Corresponding author

Correspondence to Yuheng He .

Editor information

Editors and Affiliations

  1. College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China

    Meiping Wu

  2. College of Intelligence Science and Technology, National University of Defense Technology, Changsha, Hunan, China

    Yifeng Niu

  3. Beijing HIWING Scientific and Technological Information Institute, Beijing, China

    Mancang Gu

  4. Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory, Beijing, China

    Jin Cheng

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He, Y., Wu, M., Sun, M., Wang, L. (2022). Towards Pavement Crack Detection and Classification Based on Machine Leaning Methods. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_1

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