The data article describes the Road Damage Dataset, RDD2022, encompassing of 47,420
road images from majorly six countries, Japan, India, the Czech Republic, Norway, the …

This data article provides details for the RDD2020 dataset comprising 26,336 road images
from India, Japan, and the Czech Republic with more than 31,000 instances of road …

Maintaining the roadway infrastructure is one of the essential factors in enabling a safe,
economic, and sustainable transportation system. Manual roadway damage data collection …

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 …

Monitoring road conditions is crucial for safe and efficient transportation infrastructure, but
developing effective models for automatic road damage detection is challenging requiring …

The detection, classification, and characterization of pavement cracks are critical for
maintaining safe road conditions. However, traditional manual inspection methods are slow …

Although deep-learning-based approaches have demonstrated impressive performance in
object detection tasks, the requirement for large datasets of annotated training images limits …

Automated monitoring systems have been utilized for effective road maintenance in order to
eliminate time-consuming and manual inspection by road administration employees. Image …

Road infrastructure systems have been suffering from ineffective maintenance strategies,
exaggerated by budget restrictions. A more holistic road-asset-management approach …

With the growth of the city and the increase in the number of cars, the maintenance and
management of roads attract more attention. Road damage detection of road images is the …