D
Deeksha Arya
Researcher at Indian Institute of Technology Roorkee
Publications - 16
Citations - 289
Deeksha Arya is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 4, co-authored 8 publications receiving 61 citations. Previous affiliations of Deeksha Arya include Indian Institutes of Technology & National Institute of Technology, Kurukshetra.
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Proceedings ArticleDOI
Global Road Damage Detection: State-of-the-art Solutions
Deeksha Arya,Hiroya Maeda,Sanjay Ghosh,Durga Toshniwal,Hiroshi Omata,Takehiro Kashiyama,Yoshihide Sekimoto +6 more
TL;DR: The top 12 solutions proposed by the Global Road Damage Detection Challenge are summarized, with the best performing model utilizes YOLO-based ensemble learning to yield an F1 score of 0.67 on test1 and 0.66 on test2.
Journal ArticleDOI
Deep learning-based road damage detection and classification for multiple countries
Deeksha Arya,Deeksha Arya,Hiroya Maeda,Sanjay Ghosh,Durga Toshniwal,Alexander Mraz,Alexander Mraz,Takehiro Kashiyama,Yoshihide Sekimoto +8 more
TL;DR: In this article, the authors proposed a large-scale heterogeneous road damage dataset comprising 26,620 images collected from multiple countries (India, Japan, and the Czech Republic) using smartphones.
Journal ArticleDOI
RDD2020: An annotated image dataset for automatic road damage detection using deep learning.
TL;DR: The RDD2020 dataset as mentioned in this paper contains road images from India, Japan, and the Czech Republic with more than 31,000 instances of road damage, including longitudinal cracks, transverse cracks, alligator cracks, and potholes.
Posted Content
Transfer Learning-based Road Damage Detection for Multiple Countries.
Deeksha Arya,Hiroya Maeda,Sanjay Ghosh,Durga Toshniwal,Alexander Mraz,Takehiro Kashiyama,Yoshihide Sekimoto +6 more
TL;DR: An assessment of the usability of the Japanese model for other countries is assessed and a large-scale heterogeneous road damage dataset comprising 26620 images collected from multiple countries using smartphones is proposed.
Journal ArticleDOI
RDD2022: A multi-national image dataset for automatic Road Damage Detection
Deeksha Arya,Hiroya Maeda,Sanjay Ghosh,Divya Govindprasad Toshniwal,Yoshihide Sekimoto Indian Institute of Technology Roorkee,India,T. U. O. Tokyo,Japan.,UrbanX Technologies,Inc.,Tokyo +10 more
TL;DR: 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 China, which has been annotated with more than 55,000 instances of road damage.