Road Damage Detection
13 papers with code • 1 benchmarks • 3 datasets
Road damage detection is the task of detecting damage in roads.
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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.
Pavement condition evaluation is essential to time the preventative or rehabilitative actions and control distress propagation.
In particular we show that Mask-RCNN, one of the state-of-the-art algorithms for object detection, localization and instance segmentation of natural images, can be used to perform this task in a fast manner with effective results.
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.
In this study, the authors deploy state-of-the-art deep learning algorithms based on different network backbones to detect and characterize pavement distresses.
Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world.
The results show that the X101-FPN base model for Faster R-CNN with Detectron2's default configurations are efficient and general enough to be transferable to different countries in this challenge.
Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors.
We briefly describe the tuning strategy for the experiments conducted on two-stage Faster R-CNN with Deep Residual Network (Resnet) and Feature Pyramid Network (FPN) backbone.