Automatic pothole detection

Road irregularities, such as potholes and road cracks, are a common hazard of daily commute and transportation. Automobile owners everywhere have long worried about the high price of fixing damage caused by potholes. Many accidents caused by potholes can be avoided if road repair agencies can respond quickly. Thus, an automated pothole detection system may help improve this issue. In this work, automatic pothole detection is performed using Faster R-CNN, Sparse R-CNN, YOLOv3, YOLOv5, and YOLOv7 computer vision algorithms. For this purpose, the dataset of road damage images collected in the Slovak region was used. The use of country-specific data is of great importance since the accuracy of pothole detection is dependent on the country of origin of the data. In this work, YOLOv7 achieved the highest detection accuracy of 0.884 (mAP@0.5) followed by YOLOv5 with the accuracy of 0.854 (mAP@0.5) on the pothole dataset.

• Record URL:
  • https://doi.org/10.1016/j.trpro.2023.11.257
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2352146523005549
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
• Supplemental Notes:
  • © 2023 The Author(s). Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Lieskovská, Eva
  • Jakubec, Maroš
  • Bučko, Boris
  • Zábovská, Katarína
• Conference:
  • TRANSCOM 2023: 15th International Scientific Conference on Sustainable, Modern and Safe Transport
  • Location: Mikulov , Czech Republic
  • Date: 2023-5-29 to 2023-5-31
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 1164-1170
• Serial:
  • Transportation Research Procedia
  • Volume: 74
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2352-1465
  • Serial URL: http://www.sciencedirect.com/science/journal/23521465/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Computer vision; Detection and identification; Highway maintenance; Potholes
• Identifier Terms: YOLO
• Geographic Terms: Slovakia
• Subject Areas: Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01924003
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 11 2024 1:52PM