Unsupervised Approach for Autonomous Pavement-Defect Detection and Quantification Using an Inexpensive Depth Sensor-Reference-Cited by-同舟云学术

Unsupervised Approach for Autonomous Pavement-Defect Detection and Quantification Using an Inexpensive Depth Sensor

Published:2013-11 Issue:6 Volume:27 Page:743-754
ISSN:0887-3801
Container-title:Journal of Computing in Civil Engineering
language:en
Short-container-title:J. Comput. Civ. Eng.

Author:

Jahanshahi Mohammad R.¹,Jazizadeh Farrokh²,Masri Sami F.³,Becerik-Gerber Burcin⁴

Affiliation:

1. Caltech Postdoctoral Scholar, Jet Propulsion Laboratory, California Institute of Technology, M/S 198-235, 4800 Oak Grove Dr., Pasadena, CA 91109 (corresponding author).

2. Ph.D. Candidate, Sonny Astani Dept. of Civil and Environmental Engineering, Viterbi School of Engineering, Univ. of Southern California, 3620 South Vermont Ave., KAP 217, Los Angeles, CA 90089.

3. Professor, Sonny Astani Dept. of Civil and Environmental Engineering, Viterbi School of Engineering, Univ. of Southern California, 3620 South Vermont Ave., KAP 206B, Los Angeles, CA 90089.

4. Assistant Professor, Sonny Astani Dept. of Civil and Environmental Engineering, Viterbi School of Engineering, Univ. of Southern California, 3620 South Vermont Ave., KAP 224C, Los Angeles, CA 90089.

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.1943-5487.0000245

Reference45 articles.

1. AASHTO. (2003). “Standard practice for quantifying cracks in asphalt pavement surface.” PP44-01 Washington DC.

2. ARAN. (2011). 〈http://www.roadware.com/products/〉 (Aug. 11 2011).

3. ASCE. (2009). 2009 Report card for America’s infrastructure New York.

4. ASTM. (1999). “Standard practice for roads and parking lots pavement condition index surveys.” D6433-99 West Conshohocken PA.

5. Bray J. Verma B. Li X. and He W. (2006). “A neural network based technique for automatic classification of road cracks.” IJCNN ‘06. Int. Joint Conf. on Neural Networks 2006 IEEE Piscataway NJ 907–912.

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