Road Damage Detection
13 papers with code • 1 benchmarks • 3 datasets
Road damage detection is the task of detecting damage in roads.
( Image credit: Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN )
Libraries
Use these libraries to find Road Damage Detection models and implementations
RDD-2020
Pothole Mix
NPO
Most implemented papers
Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone
sekilab/RoadDamageDetector
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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.
Transfer Learning-based Road Damage Detection for Multiple Countries
sekilab/RoadDamageDetector
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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.
FasterRCNN Monitoring of Road Damages: Competition and Deployment
code-implementation1/Code9
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Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world.
An Efficient and Scalable Deep Learning Approach for Road Damage Detection
mahdi65/roadDamageDetection2020
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Pavement condition evaluation is essential to time the preventative or rehabilitative actions and control distress propagation.
Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN
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.
Road Damage Detection Based on Unsupervised Disparity Map Segmentation
This paper presents a novel road damage detection algorithm based on unsupervised disparity map segmentation.
Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis
titanmu/RoadCrackDetection
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In this study, the authors deploy state-of-the-art deep learning algorithms based on different network backbones to detect and characterize pavement distresses.
Road Damage Detection and Classification with Detectron2 and Faster R-CNN
iDataVisualizationLab/roaddamagedetector
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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 using Deep Ensemble Learning
Road damage detection is critical for the maintenance of a road, which traditionally has been performed using expensive high-performance sensors.
CNN Model & Tuning for Global Road Damage Detection
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.
