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International Conference on Data Science

ICDS 2015: Data Science pp 95–102Cite as

Pavement Distress Detection Using Random Decision Forests

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9208))

Abstract

Pavement distress detection is a key technology to evaluate pavement surface and crack severity. However, there are many challenging problems when using pavement distress detection technology to do road maintenance, such as the inference of textured surroundings with similar intensity to the distresses, the existence of intensity inhomogeneity along the distresses and the requirement of real-time detection in practice. To address these problems, we propose a novel method for pavement distress detection based on random decision forests. By introducing the color gradient features at multiple scales commonly used in contour detection, we extend the feature set of traditional distress detection methods and get the represented crack with richer information. During the process of training, we apply a subsampling strategy at each node to maintain the diversity of trees. With this work, we finally solve all the three problems mentioned above. In addition, according to the characteristics of random decision forests, our method is easy to parallel and able to conduct real-time detection. Experimental results show that our approach is faster and more accurate than existing methods.

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Acknowledgments

This work is supported by National Natural Science Foundation of China under Grants (Grant No. 71331005, 71110107026, 61402429).

Author information

Authors and Affiliations

  1. Key Laboratory of Big Data Mining and Knowledge Management, CAS, Beijing, 100190, China

    Limeng Cui, Zhiquan Qi, Zhensong Chen, Fan Meng & Yong Shi

  2. Research Center on Fictious Economy and Data Science, CAS, Beijing, 100190, China

    Limeng Cui, Zhiquan Qi, Zhensong Chen, Fan Meng & Yong Shi

  3. School of Computer and Control Engineering, UCAS, Beijing, 100190, China

    Limeng Cui

  4. School of Management, UCAS, Beijing, 100190, China

    Zhensong Chen, Fan Meng & Yong Shi

Authors
  1. Limeng Cui
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  2. Zhiquan Qi
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  3. Zhensong Chen
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  4. Fan Meng
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  5. Yong Shi
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Corresponding authors

Correspondence to Zhiquan Qi or Yong Shi .

Editor information

Editors and Affiliations

  1. University of Technology, Sydney, Australia

    Chengqi Zhang

  2. Xi'an Jiaotong University, Xi'an Jiaotong, China

    Wei Huang

  3. Chinese Academy of Sciences, Beijing, China

    Yong Shi

  4. University of Illinois Chicago, CHICAGO, Illinois, USA

    Philip S. Yu

  5. Fudan University, Shanghai, China

    Yangyong Zhu

  6. Research Center on Fictitious Econo, Chinese Academy of Sciences, Beijing, China

    Yingjie Tian

  7. University of Technology, Sydney, Australia

    Peng Zhang

  8. Victoria University, Melbourne, Victoria, Australia

    Jing He

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Cui, L., Qi, Z., Chen, Z., Meng, F., Shi, Y. (2015). Pavement Distress Detection Using Random Decision Forests. In: Zhang, C., et al. Data Science. ICDS 2015. Lecture Notes in Computer Science(), vol 9208. Springer, Cham. https://doi.org/10.1007/978-3-319-24474-7_14

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  • DOI: https://doi.org/10.1007/978-3-319-24474-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24473-0

  • Online ISBN: 978-3-319-24474-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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