Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review

Kasthurirangan Gopalakrishnan
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Kasthurirangan Gopalakrishnan: Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, IA 50011, USA

Data, 2018, vol. 3, issue 3, 1-19

Abstract: Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms in the areas of machine translation, speech recognition, and computer vision has sparked interest in the application of deep learning to automated detection of distresses in pavement images. This paper provides a narrative review of recently published studies in this field, highlighting the current achievements and challenges. A comparison of the deep learning software frameworks, network architecture, hyper-parameters employed by each study, and crack detection performance is provided, which is expected to provide a good foundation for driving further research on this important topic in the context of smart pavement or asset management systems. The review concludes with potential avenues for future research; especially in the application of deep learning to not only detect, but also characterize the type, extent, and severity of distresses from 2D and 3D pavement images.

Keywords: pavement cracking; pavement management; pavement imaging; 3D image; deep learning; TensorFlow; deep convolutional neural networks (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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