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Found 8 papers, 3 papers with code
Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone
This dataset is composed of 9, 053 road damage images captured with a smartphone installed on a car, with 15, 435 instances of road surface damage included in these road images.
Congestion Analysis of Convolutional Neural Network-Based Pedestrian Counting Methods on Helicopter Footage
In this paper, we investigate state-of-the-art methods for counting pedestrians and the related performance of aerial footage.
City2City: Translating Place Representations across Cities
Large mobility datasets collected from various sources have allowed us to observe, analyze, predict and solve a wide range of important urban challenges.
Transfer Learning-based Road Damage Detection for Multiple Countries
Lastly, we provide recommendations for readers, local agencies, and municipalities of other countries when one other country publishes its data and model for automatic road damage detection and classification.
Global Road Damage Detection: State-of-the-art Solutions
In total, 121 teams from several countries registered for this competition.
RDD2022: A multi-national image dataset for automatic Road Damage Detection
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.
Road Rutting Detection using Deep Learning on Images
The proposed road rutting dataset and the results of our research study will help accelerate the research on detection of road rutting using deep learning.
Crowdsensing-based Road Damage Detection Challenge (CRDDC-2022)
The Big Data Cup challenges involve a released dataset and a well-defined problem with clear evaluation metrics.
