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
Latest papers
InconSeg: Residual-Guided Fusion With Inconsistent Multi-Modal Data for Negative and Positive Road Obstacles Segmentation
lab-sun/inconseg
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journal 2023
Segmentation of road obstacles, including negative and positive obstacles, is critical to the safe navigation of autonomous vehicles.
RDD2022: A multi-national image dataset for automatic Road Damage Detection
sekilab/RoadDamageDetector
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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.
Computer-Aided Road Inspection: Systems and Algorithms
Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety.
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.
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 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.
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.
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.
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.
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.
