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Pavement defect detection with fully convolutional network and an uncertainty framework
Author(s) -
Tong Zheng,
Yuan Dongdong,
Gao Jie,
Wang Zhenjun
Publication year - 2020
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12533
Subject(s) - conditional random field , segmentation , computer science , artificial intelligence , generalization , convolutional neural network , field (mathematics) , image segmentation , gaussian , process (computing) , set (abstract data type) , pattern recognition (psychology) , gaussian process , data mining , machine learning , mathematics , mathematical analysis , physics , quantum mechanics , pure mathematics , programming language , operating system
Image segmentation has been implemented for pavement defect detection, from which types, locations, and geometric information can be obtained. In this study, an integration of a fully convolutional network with a Gaussian‐conditional random field (G‐CRF), an uncertainty framework, and probability‐based rejection is proposed for detecting pavement defects. First, a fully convolutional network is designed to generate preliminary segmentation results, and a G‐CRF is used to refine the segmentation. Second, epistemic and aleatory uncertainties in the model and database are considered to overcome the disadvantages of traditional deep‐learning methods. Last, probability‐based rejection is conducted to remove unreasonable segmentations. The proposed method is evaluated on a data set of images that were obtained from 16 highways. The proposed integration segments pavement distresses from digital images with desirable performance. It also provides a satisfactory means to improve the accuracy and generalization performance of pavement defect detection without introducing a delay into the segmentation process.