Autonomous Rail Surface Defect Detection

Details

• Creators:
  Qian, Yu ; Guo, Huaqiang ; Rizos, Dimitris C ; Vitzilaios, Nikolaos
• Corporate Creators:
  University Transportation Center for Railway Safety (UTCRS) UTC ; University of South Carolina
• 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:
  • Computer vision
  • Detection and identification systems
  • Drones
  • Flaw detection
  • Railroad tracks
• Publication/ Report Number:
  UTCRS-USC-I3CY23
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States;South Carolina
• Edition:
  June 1, 2023 – August 31, 2024
• Corporate Publisher:
  University Transportation Center for Railway Safety (UTCRS) Tier-1 University Transportation Center (UTC)
• Abstract:
  The Autonomous Rail Surface Defect Detection project aims to enhance railway safety through the use of unmanned aerial vehicles (UAVs) for detecting rail surface defects. Using the RSD_UAV dataset, the project developed an optimized DeepLabv3-plus model with a ResNet-18 backbone and CBAM, achieving a mean Intersection over Union (mIOU) of 84.97% and a mean accuracy of 92.60%. The dataset, containing 13,053 images of various rail defects, was collected in Columbia, SC, and rigorously processed for model training. The system's performance was tested across different UAV flight patterns, revealing consistent defect detection but with reduced accuracy as the UAV's altitude increased. Optimal detection occurred between 3 to 9 ft above the rail, with accuracy decreasing at higher altitudes or lateral distances from the rails. This research underscores the potential of UAVs and deep learning models in advancing railway inspection and safety.
• Format:
  PDF
• Funding:
  69A3552348340
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/79170/dot_79170_DS1.pdf
• File Type:
  [PDF - 1.08 MB]
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:820d24db3fb2526fb39c603f1a5ed8f4e4f2ef1238d72777d9dcb4727587397f7b2cd86909b253e516c04eac8b61f15d21f5c535f0147f132488c2fa7f9941f0