Vision-Based Detection of Bridge Damage Captured by Unmanned Aerial Vehicles

Details

• Creators:
  Stegagno, Paolo ; Gindy, Mayrai ; Hendawi, Abdeltawab ; Licht, Stephen
• Corporate Creators:
  University of Rhode Island. Department of Electrical, Computer and Biomedical Engineering ; University of Maine. Transportation Infrastructure Durability Center (TIDC)
• Corporate Contributors:
  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:
  3D Modeling Bridge Inspection Concrete Damage Deep Learning
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report: September 2022 – September 2025
• Corporate Publisher:
  University of Maine. Transportation Infrastructure Durability Center (TIDC)
• Abstract:
  Bridge inspection is a vital component of any bridge management strategy of a state DOT. Significant funds are allocated to keeping the over 600,000 bridges in the U.S. safe. A routine bridge inspection is the most common type of inspection and is often performed from deck, ground or water levels or from permanent access structures, if available. Visual inspection is the predominant approach used in a routine inspection. With visual inspection, only basic tools for cleaning, probing, sounding, measuring, and visual aids are used. However, according to research, there can be significant variation in the condition ratings assigned to a structure simply based on visual inspection [1]. The use of unmanned aerial vehicles (UAVs) has recently been explored for the use of bridge inspections [2]. UAVs equipped with high resolution or infrared cameras can be used to scan a bridge taking hundreds of images and essentially building a navigable 3D model of the bridge. Recent advances in machine learning may be employed to automatically identify different types of bridge damage [3]. This research project will evaluate the effectiveness of using more autonomous methods for the collection and analysis of bridge deck images for the purpose of identifying the type and extent of damage in concrete decks.
• Format:
  PDF
• Funding:
  69A3551847101
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:e7a71c0247f7009de9a4675ef29e8872ba091be611fb7e4688911b999dac7e2811f72a7b21242a9c2436e2c515fb3f14b83bb752141fd746c011e12c9b743053
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88730/dot_88730_DS1.pdf
• File Type:
  [PDF - 4.32 MB ]