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Global Road Damage Detection: State-of-the-art Solutions

Global Road Damage Detection: State-of-the-art Solutions

17 November 2020
Deeksha M. Arya
Hiroya Maeda
S. Ghosh
Durga Toshniwal
Hiroshi Omata
Takehiro Kashiyama
Yoshihide Sekimoto Indian Institute of Technology Roorkee
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Papers citing "Global Road Damage Detection: State-of-the-art Solutions"

4 / 4 papers shown
Title
AI-Driven Road Maintenance Inspection v2: Reducing Data Dependency &
  Quantifying Road Damage
AI-Driven Road Maintenance Inspection v2: Reducing Data Dependency & Quantifying Road Damage
Haris Iqbal
Hemang Chawla
Arnav Varma
Terence Brouns
A. Badar
Elahe Arani
Bahram Zonooz
34
0
0
07 Oct 2022
RDD2022: A multi-national image dataset for automatic Road Damage
  Detection
RDD2022: A multi-national image dataset for automatic Road Damage Detection
Deeksha M. Arya
Hiroya Maeda
S. Ghosh
Durga Toshniwal
Inc.
14
111
0
18 Sep 2022
FasterRCNN Monitoring of Road Damages: Competition and Deployment
FasterRCNN Monitoring of Road Damages: Competition and Deployment
T. Hascoet
Yihao Zhang
Andreas Persch
R. Takashima
T. Takiguchi
Y. Ariki
15
23
0
22 Oct 2020
Deep Learning Frameworks for Pavement Distress Classification: A
  Comparative Analysis
Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis
Vishal Mandal
Abdul Rashid Mussah
Y. Adu-Gyamfi
24
53
0
21 Oct 2020
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