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 )


Use these libraries to find Road Damage Detection models and implementations

Most implemented papers

Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone

sekilab/RoadDamageDetector 29 Jan 2018

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.

An Efficient and Scalable Deep Learning Approach for Road Damage Detection

mahdi65/roadDamageDetection2020 18 Nov 2020

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

sshkhr/BigDataCup18_Submission 12 Nov 2018

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

ruirangerfan/unsupervised_disparity_map_segmentation 11 Oct 2019

This paper presents a novel road damage detection algorithm based on unsupervised disparity map segmentation.

Transfer Learning-based Road Damage Detection for Multiple Countries

sekilab/RoadDamageDetector 30 Aug 2020

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.

Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis

titanmu/RoadCrackDetection 21 Oct 2020

In this study, the authors deploy state-of-the-art deep learning algorithms based on different network backbones to detect and characterize pavement distresses.

FasterRCNN Monitoring of Road Damages: Competition and Deployment

MindSpore-paper-code-2/code400 22 Oct 2020

Maintaining aging infrastructure is a challenge currently faced by local and national administrators all around the world.

Road Damage Detection and Classification with Detectron2 and Faster R-CNN

iDataVisualizationLab/roaddamagedetector 28 Oct 2020

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

kevaldoshi17/IEEE-Big-Data-2020 30 Oct 2020

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

vishwakarmarhl/rdd2020 17 Mar 2021

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.