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.
Highlights
Transportation infrastructure systems are essential to the minimum operations of the government and commerce, and are considered the backbone of a nation’s economy
Portlandvehicle cementmoving concrete (PCC)-surfaced pavement image captured using pavement data collection at highway speed and equippeddistress with a downward-looking pavement data collection vehicle moving at highway speed and equipped with a downward-looking high-speed digital camera (Source: FHWA LTPP database)
Zhang et al [33] demonstrated that their developed ConvNet, which has automatically learned to discriminate between image patches with and without cracks and without consideration of the pavement geometry, was able to outperform support vector machine (SVM) and the boosting methods in crack detection of patch images with a precision of 0.8696, recall of 0.9251, F1 score of 0.8965, and an area-under-the-ROC-curve (AUC) of 0.9592 [6]
Summary
Transportation infrastructure systems are essential to the minimum operations of the government and commerce, and are considered the backbone of a nation’s economy They are literally crumbling across the globe and are considered “to be on life support” even by authorities in charge of maintaining them, especially in the United States. This, combined with increasing budgetary constraints, necessitates the development of efficient structural and functional health monitoring techniques for early detection of distresses developing in pavements. This can lead to significant cost savings resulting from timely maintenance and repair activities. The challenges associated with 2D pavement images, such as variations in image three decades.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
