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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Veröffentlicht in:	Automation in construction Jg. 133; S. 103991
Hauptverfasser:	Zhu, Junqing, Zhong, Jingtao, Ma, Tao, Huang, Xiaoming, Zhang, Weiguang, Zhou, Yang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.01.2022 Elsevier BV
Schlagworte:	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-Zugang:	Volltext
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