[1] Wang Meng, Zhang Xiaoyue, Liu Cheng, Xu Huitong, et al. Intelligent detection of cracks on cement pavements of rural highways [J]. Journal of Southeast University (English Edition), 2023, 39 (4): 340-349. [doi:10.3969/j.issn.1003-7985.2023.04.003]

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Intelligent detection of cracks on cement pavements of rural highways()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

39

Issue:

2023 4

Page:

340-349

Research Field:

Traffic and Transportation Engineering

Publishing date:

2023-12-20

Info

Title:

Intelligent detection of cracks on cement pavements of rural highways

Author(s):

Wang Meng¹;  Zhang Xiaoyue¹;  Liu Cheng²;  Xu Huitong¹;  Yang Yanze¹

¹ School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
² China-Road Transportation Verification and Inspection Hi-Tech Co., Ltd., Beijing 100088, China

Keywords:

rural highway;  cement pavement;  crack;  deep learning;  image classification;  object detection

PACS:

U416.216

DOI:

10.3969/j.issn.1003-7985.2023.04.003

Abstract:

Traditional artificial image processing methods suffer from problems in the detection of damage in rural highway pavements, such as low efficiency, nonobjective results, and the inability to process a large amount of data in time. To solve these problems, an intelligent method is proposed for the detection of cracks on rural cement pavements. The proposed method is integrated with ResNet50 for pavement classification and an improved YOLOv5 crack detection algorithm, considering the distribution characteristics of rural highway sections. Different training strategies and different network depth were compared to construct an efficient pavement classification model based on ResNet50 with the aim of automatically identifying cements and asphalt pavements in rural highways. A dataset that contains 18 028 pieces of crack detection data for cement pavements of rural highways was created. A comparative experimental study of single- and two-stage object detection algorithm was performed, and the optimal detection algorithm with both detection accuracy and efficiency was obtained. Furthermore, the adaptive spatial feature fusion strategy and the optimized regression loss function are integrated into the optimization algorithm to effectively solve the problem of multi-scale crack leakage detection in the image, and further improve the overall detection accuracy. The integrated method was applied to the field measurement of real cement pavements of rural highways. The results demonstrate that the accuracy of pavement type classification is 98.4% and that of crack detection is 93.0%, indicating that the proposed method can provide accurate and efficient solutions for the detection of cement pavements of rural highways.

References:

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Memo

Memo:

Biography: Wang Meng(1985—), female, doctor, professor, wangmeng@bjtu.edu.cn.
Foundation items: Beijing Nova Program(No. 20220484103), Beijing Municipal Natural Science Foundation(No. 8222027), the Fundamental Research Funds for the Central Universities(No. 2022YJS071).
Citation: Wang Meng, Zhang Xiaoyue, Liu Cheng, et al. Intelligent detection of cracks on cement pavements of rural highways[J].Journal of Southeast University(English Edition), 2023, 39(4):340-349.DOI:10.3969/j.issn.1003-7985.2023.04.003.

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