RDD2020: An annotated image dataset for automatic road damage detection using deep learning

This data article provides details for the RDD2020 dataset comprising 26,336 road images from India, Japan, and the Czech Republic with more than 31,000 instances of road damage. The dataset captures four types of road damage: longitudinal cracks, transverse cracks, alligator cracks, and potholes; a...

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Vydáno v:	Data in brief Ročník 36; s. 107133
Hlavní autoři:	Arya, Deeksha, Maeda, Hiroya, Ghosh, Sanjay Kumar, Toshniwal, Durga, Sekimoto, Yoshihide
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Inc 01.06.2021 Elsevier
Témata:	alligators Automatic road condition monitoring Crack recognition Czech Republic Data data collection Data qualification Deep learning image analysis India Japan mobile telephones Pavement surface condition assessment pavements Quantification Road damage dataset Road infrastructure Smartphone-based road damage detection and classification Structural health monitoring Czech Republic India Deep learning Road damage dataset Smartphone-based road damage detection and classification Quantification Data qualification Structural health monitoring Crack recognition Image Road infrastructure Automatic road condition monitoring Pavement surface condition assessment
ISSN:	2352-3409, 2352-3409
On-line přístup:	Získat plný text
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