Deep Learning Based Automated Data Collection Technology for Coastal Highway Pavements

Details

• Creators:
  Wang, Feng ; Sanchez, Carlos ; Bai, Yongsheng ; Luo, Xiaohua
• Corporate Creators:
  Texas State University--San Marcos
• Corporate Contributors:
  CREATE - Coastal REsearch And Transportation Education ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Algorithms Artificial Intelligence Automatic Data Collection Systems Coasts Data Files Machine Learning Pavement Maintenance
• Publication/ Report Number:
  2301-6
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States ; Louisiana ; Mississippi ; Texas
• Edition:
  Final Project Report 09/01/2023-8/31/2025
• Corporate Publisher:
  CREATE - Coastal REsearch And Transportation Education
• Abstract:
  With the recent federal legislation approval of the funding of the United States’s infrastructure, investment into transportation research has prompted the implementation of new technologies to advance the data collection and evaluation methods for transportation asset management. As departments of transportation (DOTs) upgrade their equipment to utilize these technologies, a higher standard for the quality of highway infrastructure across the states has raised concerns for an improvement in traffic safety and efficient maintenance and rehabilitation of the roadways. This is essential for pavements in locations where surface distresses are developed at a higher rate, such as coastal regions due to rapid development and extreme weather phenomena being more frequent and intensive. State DOTs have adopted the use of automated pavement data collection and condition evaluation. However, the inadequate accuracy of the existing automated technology has led to erroneous distress measurements and inconsistent manual intervention approaches for pavement engineers to assess the surface damage of the pavements after data collection to establish more reliable analyses of the performances. With the recent advancements in artificial intelligence and deep learning, progress towards more accurate and efficient detection algorithms has provided possibilities of better data quality to monitor the surface condition of the pavements. Due to these new technologies, 2D/3D pavement images can be analyzed with improved accuracy for more efficient detection of pavement distresses such as cracks caused by various factors like environment, weather, age, and traffic loading. In this study, 2D/3D images were collected from pavements in the coastal regions in the states of Louisiana, Mississippi, and Texas.
• Format:
  PDF
• Funding:
  69A3552348330
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:714c426eaec9ef94fbdcd3068789d495d71dfbb57e947d6687bd35958a3794e49d2b7caf36a1ff1c27829cdd81842e353727f9a3cd66f2a582e9e84489e79d91
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88180/dot_88180_DS1.pdf
• File Type:
  [PDF - 2.56 MB ]