Automated Defect Quantification in Concrete Bridges Using Robotics and Deep Learning-Reference-Cited by-同舟云学术

Automated Defect Quantification in Concrete Bridges Using Robotics and Deep Learning

Published:2020-09 Issue:5 Volume:34 Page:04020029
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

McLaughlin Evan¹,Charron Nicholas²,Narasimhan Sriram³

Affiliation:

1. M.A.Sc. Candidate, Dept. of Civil and Environmental Engineering, Univ. of Waterloo, Waterloo, ON, Canada N2L 3G1.

2. Ph.D. Candidate, Dept. of Civil, Environmental Engineering, Mechanical, and Mechatronics Engineering, Univ. of Waterloo, Waterloo, ON, Canada N2L 3G1.

3. Professor, Dept. of Civil, Environmental Engineering, Mechanical, and Mechatronics Engineering, Univ. of Waterloo, Waterloo, ON, Canada N2L 3G1 (corresponding author).

Publisher

American Society of Civil Engineers (ASCE)

Subject

Computer Science Applications,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29CP.1943-5487.0000915

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3. A method for registration of 3-D shapes

4. The Surveyor's Area Formula

5. Censi A. 2007. “An accurate closed-form estimate of ICP’S covariance.” In Proc. IEEE Int. Conf. on Robotics and Automation 3167–3172. New York: IEEE.

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