Assessment of Work Zone Pre-crash Scenarios Using Crowdsourced Data

Pavements play a vital role in the transportation infrastructure in the United States. Damage to public road transportation infrastructure causes roadways to fail to perform as intended and increases crash risks. Road damage must be detected quickly and accurately in order to maintain roads and effectively allocate repair money. In this study, four deep-learning object detection models were used for detecting five types of pavement damages using Nexar Dashcam images. The single-shot multi-box detector (SSD) and faster region-based convolutional neural networks (Faster R-CNN) object detection models using MobileNet and Inception techniques were applied to the selected and processed images. An attempt was made to detect alligator cracking, longitudinal cracking, transverse cracking, and patching on the roadway surface using the developed model. A total number of 6,326 images with a total number of 8,970 damage instances were selected from the provided data. Faster R-CNN models showed higher recall and precision compared to the SSD models. Faster R-CNN Inception Resnet showed the highest average precision of 56%, followed by Faster R-CNN Inception with an average precision of 50%. Recall values showed almost the same trend as precision. Faster R-CNN showed the highest average recall of 43%. The results showed that Type-I and Type-II damages were detected with the highest recall and precision compared to other damages. Type-I damage was detected with an average precision of 81% and recall of 70 % in all of the models.

Language

  • English

Project

  • Status: Completed
  • Funding: $39,100
  • Contract Numbers:

    69A3551747115

  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Safety through Disruption University Transportation Center (Safe-D)

    Virginia Tech Transportation Institute
    Blacksburg, VA  United States  24060
  • Project Managers:

    Glenn, Eric

  • Performing Organizations:

    Texas A&M Transportation Institute (TTI)

    400 Harvey Mitchell Parkway South
    Suite 300
    College Station, TX  United States  77845-4375
  • Principal Investigators:

    Dadashova , Bahar

  • Start Date: 20200820
  • Expected Completion Date: 20210515
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

  • TRT Terms: Machine vision
  • Subject Areas: Pavements; Safety and Human Factors;

Filing Info

  • Accession Number: 01778958
  • Record Type: Research project
  • Source Agency: Safety through Disruption University Transportation Center (Safe-D)
  • Contract Numbers: 69A3551747115
  • Files: UTC, RIP
  • Created Date: Aug 11 2021 11:24PM