162 citations · 232 across the 2 of their papers we have counts for
5 papers
Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)
Deeksha Arya, Hiroya Maeda, Sanjay Kumar Ghosh +4
This paper summarizes the Crowdsensing-based Road Damage Detection Challenge (CRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2022. The…
RDD2022: A multi-national image dataset for automatic Road Damage Detection
Deeksha Arya, Hiroya Maeda, Sanjay Kumar Ghosh +2
The data article describes the Road Damage Dataset, RDD2022, which comprises 47,420 road images from six countries, Japan, India, the Czech Republic, Norway, the United States, and…
Global Road Damage Detection: State-of-the-art Solutions
Deeksha Arya, Hiroya Maeda, Sanjay Kumar Ghosh +4
This paper summarizes the Global Road Damage Detection Challenge (GRDDC), a Big Data Cup organized as a part of the IEEE International Conference on Big Data'2020. The Big Data Cup…
Transfer Learning-based Road Damage Detection for Multiple Countries
Deeksha Arya, Hiroya Maeda, Sanjay Kumar Ghosh +4
Many municipalities and road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art…
Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone
Hiroya Maeda, Yoshihide Sekimoto, Toshikazu Seto +2
Research on damage detection of road surfaces using image processing techniques has been actively conducted, achieving considerably high detection accuracies. Many studies only foc…