1. Home
  2. RIP
  3. View Record
https://www.nationalacademies.org/trb/events

Use of Advanced Data Capture Tools on Measurements of Crack Lengths and Potholes for Estimates and Final Quantities

According to the Pavement Management Information System, the Kansas Department of Transportation (KSDOT) maintains 11,357 miles of pavement (counting miles in both directions of divided highways). About 90% of this mileage is asphalt pavement. KSDOT’s contract maintenance work related to crack sealing, pot-hole patching, etc., is common for these pavements. The current measurement techniques use a measuring wheel, distance measuring instrument (DMI), etc. These techniques are highly susceptible to human errors and utilize considerable time and manpower. They also obstruct the traffic flow while conducting roadway measurements and putting the personnel at risk. The KSDOT idea submitted cites data collection via high-accuracy drone surveys but drone operations are restricted on KSDOT right of ways to prevent traveler distraction. Recent developments in camera technology and high-speed, high-resolution image capture at an affordable cost offer the opportunity for automation of measurements of crack lengths and potholes/patches for estimates and final quantities. Example camera models include Vantrue S1 Pro 2.7K Front and Rear 5G WiFi Dash Cam, VIOFO Dash Cam Front and Rear 2K 1440P 60fps, Dash Cam Front and Rear - POFOTO 2.5K 1440P 60fps and 1080P 30fps Dash Camera, VIOFO A129 Plus Dash Cam 2K 1440P 60FPS GPS Wi-Fi Car Dash Camera with HDR and equivalent. These cameras all cost less than $250. The challenge lies in processing the images. However, with recent developments in artificial intelligence and machine learning, this problem can be resolved relatively quickly. One such algorithm for spatial pattern analysis is Convolutional Neural Networks (CNN), which have developed rapidly and have been applied in computer vision, natural language processing, and other fields. The convolutional neural network mimics the biological visual perception mechanism and can carry out supervised and unsupervised learning. However, traditional CNN has some drawbacks, like as the number of layers increases, the quality of the model decreases, ultimately leading to a decline in supervised learning accuracy. Thus, newer algorithms based on CNN have been developed that will be deployed in this study.

    Language

    • English

    Project

    • Status: Active
    • Funding: $83,082.00
    • Contract Numbers:

      K-TRAN: KSU-26-4

      RE-0922-01

      C2254

    • Sponsor Organizations:

      Kansas Department of Transportation

      Eisenhower State Office Building
      700 SW Harrison Street
      Topeka, KS  United States  66603-3754
    • Performing Organizations:

      Kansas State University

      Manhattan, KS  United States  66506
    • Principal Investigators:

      Hossain, Mustaque

    • Start Date: 20250801
    • Expected Completion Date: 20270131
    • Actual Completion Date: 0

    Subject/Index Terms

    Filing Info

    • Accession Number: 01976241
    • Record Type: Research project
    • Source Agency: Kansas Department of Transportation
    • Contract Numbers: K-TRAN: KSU-26-4, RE-0922-01, C2254
    • Files: RIP, STATEDOT
    • Created Date: Jan 13 2026 4:08PM