877 citations · 1.1k across the 6 of their papers we have counts for
6 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…
Pseudo-PFLOW: Development of nationwide synthetic open dataset for people movement based on limited travel survey and open statistical data
Takehiro Kashiyama, Yanbo Pang, Yoshihide Sekimoto +1
People flow data are utilized in diverse fields such as urban and commercial planning and disaster management. However, people flow data collected from mobile phones, such as using…
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…
Congestion Analysis of Convolutional Neural Network-Based Pedestrian Counting Methods on Helicopter Footage
Gergely Csönde, Yoshihide Sekimoto, Takehiro Kashiyama
Over the past few years, researchers have presented many different applications for convolutional neural networks, including those for the detection and recognition of objects from…
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…