Integration of Small Unmanned Aircraft Systems and Deep Learning for Efficient Airfield Pavement Crack Detection and Assessment-Reference-Cited by-同舟云学术

Integration of Small Unmanned Aircraft Systems and Deep Learning for Efficient Airfield Pavement Crack Detection and Assessment

Published:2024-06-13 Issue: Volume: Page:884-893
ISSN:
Container-title:International Conference on Transportation and Development 2024
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Author:

Sourav Md. Abdullah All¹²³⁴,Ceylan Halil¹²³⁴,Kim Sunghwan¹²³⁴,Brynick Matthew¹²³⁴

Affiliation:

1. Postdoc Research Associate, Dept. of Civil, Construction, and Environmental Engineering, Iowa State Univ., Ames, IA.

2. Professor and Director, Dept. of Civil, Construction, and Environmental Engineering and Program for Sustainable Pavement Engineering and Research, Iowa State Univ., Ames, IA.

3. Associate Director, Program for Sustainable Pavement Engineering and Research, Institute for Transportation, Iowa State Univ., Ames, IA.

4. Civil Engineer, Federal Aviation Administration, Airport Pavement R&D Section, William J. Hughes Technical Center, NJ.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485514.078

Reference28 articles.

1. Airport Improvement. (2020). Savannah/Hilton Head Int’l Prepares to Integrate Drones Into Airport & Airfield Operations | Airport Improvement Magazine.

2. Airsight. (2020). Runway pavement inspections using airsight drone. https://www.airsight.de/projects/item/runway-pavement-inspections-using-airsight-drone/.

3. Banks E. et al. (2018). Successful Approaches for the Use of Unmanned Aerial System by Surface Transportation Agencies. https://domesticscan.org/download/4364/.

4. Congress S. S. C. and Puppala A. J. (2022). Lessons Learned in Airport Asset Inspection Using Unmanned Aerial Vehicle (UAV) Based Close-Range Photogrammetry. International Conference on Transportation and Development 2022 212–222.