A Method for Surveying Road Pavement Distress Based on Front-View Image Data Using a Lightweight Segmentation Approach

The utilization of low-cost video data is becoming more prevalent in pavement surveys to meet the increasing demand for timely distress detection and repair. Semantic segmentation algorithms can effectively segment pavement features and distresses simultaneously. Previous studies on pavement distress segmentation have primarily focused on cracks, and most multiobjective segmentation algorithms are not accurate or efficient. This paper presents a new method for pavement segmentation using a lightweight network segmentation model that employs DeepLabV3+ with MobileNetV2 as the backbone and a convolution block attention module to extract effective information in the encoder. The authors constructed a self-created data set called ChongQing University Pavement management (CQUPM), which includes five pavement features and six types of distress. Based on the CQUPM data set and a publicly available data set, RTK, the proposed model demonstrates superior accuracy and complexity compared to DeepLabv3+,  U-Net, and Segformer-b3. Its lightweight nature is particularly noteworthy, with a parameter size of only about 1/10 to 1/4 that of other models based on the same data set. The case analysis highlights the exceptional performance of the proposed model, especially in scenarios where multiple types of pavement distress overlap. Furthermore, the model excels in edge segmentation and shows good generalization performance, indicating strong potential for practical applications.

• Record URL:
  • https://doi.org/10.1061/JCCEE5.CPENG-5868
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/08873801
• Supplemental Notes:
  • © 2024 American Society of Civil Engineers.
• Authors:
  • Yang, Yuanji
  • Wang, Hui
  • Kang, Junyang
  • Xu, Zhoucong
• Publication Date: 2024-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 04024026
• Serial:
  • Journal of Computing in Civil Engineering
  • Volume: 38
  • Issue Number: 5
  • Publisher: American Society of Civil Engineers
  • ISSN: 0887-3801
  • Serial URL: https://ascelibrary.org/journal/jccee5

Subject/Index Terms

• TRT Terms: Image analysis; Pavement distress; Pavement management systems; Video imaging detectors
• Geographic Terms: Chongqing (China)
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Pavements;

Filing Info

• Accession Number: 01924774
• Record Type: Publication
• Files: TRIS, ASCE
• Created Date: Jul 19 2024 4:40PM