Pavement distress detection using convolutional neural networks with images captured via UAV

Pavement distress detection is crucial in the decision-making for maintenance planning. Unmanned aerial vehicles (UAVs) are helpful in collecting pavement images. This paper proposes the collection of pavement distress information using a UAV with a high-resolution camera. A UAV platform for pavemen...

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Bibliographic Details
Published in:	Automation in construction Vol. 133; p. 103991
Main Authors:	Zhu, Junqing, Zhong, Jingtao, Ma, Tao, Huang, Xiaoming, Zhang, Weiguang, Zhou, Yang
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.01.2022 Elsevier BV
Subjects:	Algorithms Artificial neural networks Asphalt pavement distress Convolutional neural network (CNN) Datasets Decision making Image quality Inspection Object-detection algorithms Pavements Unmanned aerial vehicle (UAV) Unmanned aerial vehicles Unmanned aerial vehicle (UAV) Convolutional neural network (CNN) Asphalt pavement distress Object-detection algorithms
ISSN:	0926-5805, 1872-7891
Online Access:	Get full text
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