Automatic Pavement Type Recognition for Image-Based Pavement Condition Survey Using Convolutional Neural Network

Guangwei Yang, Kelvin C.P. Wang, Joshua Qiang Li, Yue Fei, Yang Liu, Kamyar C. Mahboub, Allen A. Zhang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Image-based systems are becoming popular to collect pavement condition data for pavement management activities. Pavement engineers define various distress categories based on pavement types. However, software solutions today have limitations in correctly recognizing pavement types from the collected images in an automated way. This paper presents a convolutional neural network (CNN)-based PvmtTPNet to automatically recognize pavement types at acceptable levels of consistency, accuracy, and high-speed. Pavement images on asphalt concrete pavements, jointed plain concrete pavements, and continuously reinforced concrete pavements in varying conditions were collected via the PaveVision3D system in 2018. A total number of 21,000 two-dimensional (2D) images were prepared, while 80% and 20% of them were randomly selected for training and testing. The CNN network included six layers with 992,979 tuned hyperparameters and achieved 99.85% and 98.37% prediction accuracies for training and testing in pavement type recognition. Images obtained from another two data collections in 2019 were used to validate the PvmtTPNet, and 91.27% and 96.66% prediction accuracies were reached, individually. In addition, the PvmtTPNet shows the highest precision, recall, and F1-score for asphalt concrete (AC) images, which is followed by jointed plain concrete pavement (JPCP) and continuously reinforced concrete pavement (CRCP) images. The developed methodology can provide substantial assistance toward a fully automated pavement condition data analysis for image-based systems, even though a near 100% accuracy is the final objective of the continuing research.

Original languageEnglish
Article number04020060
JournalJournal of Computing in Civil Engineering
Volume35
Issue number1
DOIs
StatePublished - Jan 1 2021

Bibliographical note

Funding Information:
The research presented in the paper was partially funded under the research project, “Developing Standard Definitions for Comparable Pavement Cracking Data,” sponsored by the National Cooperative Highway Research Program (NCHRP), as well as other active Federal Highway Administration (FHWA) and Oklahoma Department of Transportation (DOT) projects in 2018 and 2019. The opinions expressed in the paper are those of the authors, who are responsible for the accuracy of the presented data in this study, and do not necessarily reflect the official policies of the sponsoring agencies. This paper does not constitute a standard, regulation, or specification.

Publisher Copyright:
© 2020 American Society of Civil Engineers.

Keywords

  • Convolutional neural network
  • Deep learning
  • Image-based pavement survey
  • Pavement condition data
  • Pavement management
  • Pavement type

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Automatic Pavement Type Recognition for Image-Based Pavement Condition Survey Using Convolutional Neural Network'. Together they form a unique fingerprint.

Cite this