Technical Papers
Mar 3, 2016

Integrated Processing of Image and GPR Data for Automated Pothole Detection

Publication: Journal of Computing in Civil Engineering
Volume 30, Issue 6

Abstract

A pothole is a severe pavement distress that can compromise pavement rideability and safety and can be the cause of expensive damage claims. The detection and evaluation of potholes are predominantly manual and time-consuming. Although sensing technologies such as global positioning systems (GPS), stereovision systems, and ground penetrating radar (GPR) now can be combined to collect pavement condition data for assessment, the raw data returned by these sensors are often processed individually and separately. This isolated approach to data processing hinders the potential efficiency and effectiveness of multisensor systems. This paper proposes a method to integrate the processing of two-dimensional images and GPR data to automate accurate and efficient pothole detection. First, the images and GPR scans are preprocessed to filter out noise and enhance the essential clues related to potholes. Second, a novel pothole detector was designed by investigating the patterns of GPR signals reflected by potholes. Third, the position and dimension of the detected pothole can be estimated from GPR data and mapped to the image to enable a localized shape segmentation. The proposed method was validated through 50 experiments. The precision, recall, and accuracy achieved were 94.7, 90, and 88%, respectively. The mean and standard deviation of error percentage in pothole shape extraction were 12.8 and 6.5%, respectively. The method and results reported in this paper demonstrate that integrated and complementary processing of multisensory data can be achieved by channeling data streams and linking data processing according to the merits of the individual sensors.

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Acknowledgments

This research was funded by the National Science Foundation (NSF) via Grant CMMI-1265895 and Grant CMMI-1462638. The authors gratefully acknowledge NSF’s support. Any opinions, findings, conclusions, and recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF, Purdue University, and Tianjin University.

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Go to Journal of Computing in Civil Engineering
Journal of Computing in Civil Engineering
Volume 30Issue 6November 2016

History

Received: Aug 13, 2015
Accepted: Dec 30, 2015
Published online: Mar 3, 2016
Discussion open until: Aug 3, 2016
Published in print: Nov 1, 2016

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Authors

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Ph.D. Student, Division of Construction Engineering and Management, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907. E-mail: [email protected]
Chenxi Yuan [email protected]
Ph.D. Student, Division of Construction Engineering and Management, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907. E-mail: [email protected]
Donghai Liu [email protected]
Professor, State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin Univ., 92 Weijin Rd., Nankai District, Tianjin 300072, China. E-mail: [email protected]
Associate Professor, Division of Construction Engineering and Management, Lyles School of Civil Engineering, Purdue Univ., 550 Stadium Mall Dr., West Lafayette, IN 47907 (corresponding author). E-mail: [email protected]

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