Intelligent identification of asphalt pavement cracks based on semantic segmentation

被引：1
|
作者：
Yang Y.-Z. [1 ]
Wang M. [1 ]
Liu C. [2 ]
Xu H.-T. [1 ]
Zhang X.-Y. [1 ]
机构：
[1] School of Civil Engineering, Beijing Jiaotong University, Beijing
[2] China Road Transportation Verification and Inspection Hi-Tech Co Ltd., Beijing
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2023年 / 57卷 / 10期
关键词：
asphalt pavement inspection; convolution neural network; crack identification; intersection over union; semantic segmentation;
D O I：
10.3785/j.issn.1008-973X.2023.10.018
中图分类号：
学科分类号：
摘要：
An intelligent method of asphalt pavement crack recognition based on semantic segmentation was proposed, solving the shortcomings of traditional manual inspection of asphalt pavement, such as low efficiency and lack of objectivity. Considering the effects of data set size, algorithm type, network type and depth, and loss function type, the optimal crack intelligent identification scheme and corresponding model were proposed for both large and small scale data sets through the comparative study of 22 semantic segmentation models. Based on the asphalt pavement of sixth ring road in Beijing, the crack segmentation dataset R-Crack was established. The proposed intelligent identification scheme was verified and the crack parameters were automatically quantified. Results showed that the highest detection accuracy reached 83.45%. The average errors of crack length and width were 2.84% and 2.39% respectively by comparing the calculation results of crack parameters obtained through manual and automatic detection methods, The proposed intelligent recognition scheme provided a basis for the intelligent detection practice of asphalt pavement cracks in the expressway and other scenes. © 2023 Zhejiang University. All rights reserved.
引用
收藏
页码：2094 / 2105
页数：11
相关论文
共 35 条
  • [1] 2020 national toll road statistics bulletin [J], Transportation Finance and Accounting, 2021, 11, pp. 93-96
  • [2] XU Peng, ZHU Xuan, YAO Ding, Et al., Review on intelligent detection and decision-making of asphalt pavement maintenance [J], Journal of Central South University: Science and Technology, 52, 7, pp. 2099-2117, (2021)
  • [3] MA Jian, ZHAO Xiang-mo, HE Shuan-hai, Et al., Review of pavement detection technology [J], Journal of Traffic and Transportation Engineering, 17, 5, pp. 121-137, (2017)
  • [4] pp. 17-21, (2018)
  • [5] pp. 11-14, (2014)
  • [6] LI Wei, ZHU Ping-zhe, Image detection algorithm research for asphalt pavement crack [J], Computer Engineering and Applications, 48, 19, pp. 163-166, (2012)
  • [7] WENG Piao, LU Yan-hui, QI Xian-biao, Et al., Pavement crack segmentation technology based on improved fully convolutional networks [J], Computer Engineering and Applications, 55, 16, pp. 235-239, (2019)
  • [8] ZHAO Ying, Pavement crack segmentation technology based on improved fully convolutional networks, pp. 9-13, (2021)
  • [9] TANG You-zhi, Research on intelligent cracks detection algorithm of asphalt pavement based on convolutional neural network, pp. 13-22, (2021)
  • [10] HSIEH Y A, TSAI Y J., Machine learning for crack detection: review and model performance comparison, Journal of Computing in Civil Engineering, 34, 5, (2021)
1234