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XU Zhi-gang, CHE Yan-li, LI Jin-long, ZHAO Xiang-mo, PAN Yong, WANG Zhong-ren, WEI Na, SONG Hong-xun. Research progress on automatic image processing technology for pavement distress[J]. Journal of Traffic and Transportation Engineering, 2019, 19(1): 172-190. doi: 10.19818/j.cnki.1671-1637.2019.01.017
Citation: XU Zhi-gang, CHE Yan-li, LI Jin-long, ZHAO Xiang-mo, PAN Yong, WANG Zhong-ren, WEI Na, SONG Hong-xun. Research progress on automatic image processing technology for pavement distress[J]. Journal of Traffic and Transportation Engineering, 2019, 19(1): 172-190. doi: 10.19818/j.cnki.1671-1637.2019.01.017
shu

Research progress on automatic image processing technology for pavement distress

doi: 10.19818/j.cnki.1671-1637.2019.01.017
  • 1.

    School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    Shijiazhuang Huayan Transportation Technology Co., Ltd., Xi'an Branch, Xi'an 710000, Shaanxi, China

  • 3.

    Zhongzi Data Co., Ltd., Beijing 100083, China

  • 4.

    Division of Maintenance, California Department of Transportation, Sacramento 95835, California, USA

More Information
  • Author Bio:

    XU Zhi-gang(1979-), male, professor, PhD, xuzhigang@chd.edu.cn

  • Received Date: 2018-08-22
  • Publish Date: 2019-02-25
    Abstract
  • The important research achievements on the automatic image processing technology for pavement distress were summarized. The research progress of key technologies in this field was analyzed, including the pavement distress detection system, image processing algorithm and evaluation of recognition algorithm. The detection accuracy and applicability were compared for the different pavement distress detection systems and target automatic recognition algorithms. The possible future research directions of automatic pavement distress image processing technology were presented. Research result shows that in the aspect of pavement distress detection system, from early image acquisition based on the photography technology to the current 3D laser scanning technology, the pavement image acquisition technology becomes more and more convenient and effective. However, there still exist some challenges in the automatic analysis on distress images and automatic recognition algorithm on targets. In the aspect of pavement distress image processing algorithm, the traditional algorithms of segmenting pavement distress targets evolve from the methods using single feature (such as grayscale and edge shape) to multi-feature fusion-based methods and graph optimization-based detection methods. Furthermore, there emerges some dedicated algorithms for recovering or connecting cracks, greatly improving the detection accuracy of crack recognition. Nonetheless, as the complexity of these algorithms grows up, the required computational resources and the size of prior knowledge base both sharply increase. In the aspect of evaluation and comparison of crack processing algorithms, manual segmentation is mainly used to evaluate automatic recognition results. At present, it is urgent to establish a large-scale pavement distress image database opening to the world, so as to objectively and effectively evaluate various existing image processing algorithms for pavement distress. Automatic image processing algorithms for pavement distress based on 2D image features analysis is difficult to achieve the best results with detection accuracy, algorithm versatility and real-time performance simultaneously. In recent years, a large number of scholars begin to use the deep learning neural network to automatically recognize pavement distress, but the technology is still in an active evolution process. In the aspect of improving the accuracy and efficiency of automatic recognition for pavement distress, the 3D laser scanning technology and the deep learning technology based on artificial intelligence will greatly promote the final breakthrough on automatic image recognition technology for pavement distress in the future.

     

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