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computer-vision-6

Road Damage Detection

3260 papers • 126 benchmarks • 313 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 )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in road-damage-detection-6

Trend

Dataset

Best Model

Actions

NPO

NPO

Libraries

i

Use these libraries to find road-damage-detection-6 models and implementations

sekilab/RoadDamageDetector

3 papers 695

Datasets

RDD-2020

Pothole Mix

NPO

Subtasks

No subtasks available.

Most implemented papers

Transfer Learning-based Road Damage Detection for Multiple Countries

Takehiro Kashiyama, Hiroya Maeda, Deeksha M. Arya, S. Ghosh, Durga Toshniwal, A. Mraz, Yoshihide Sekimoto Indian Institute of Technology Roorkee, India, T. U. O. Tokyo, Japan., EU Amazon, Luxembourg•Sat Aug 29 2020

An assessment of the usability of the Japanese model for other countries is assessed and a large-scale heterogeneous road damage dataset comprising 26620 images collected from multiple countries using smartphones is proposed.

82

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

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An Efficient and Scalable Deep Learning Approach for Road Damage Detection

Sadra Naddaf-sh, M.-Mahdi Naddaf-Sh, Amir R. Kashani, H. Zargarzadeh•Tue Nov 17 2020

A deep learning-based surveying scheme to analyze the image-based distress data in real-time and propose efficient and scalable models that are tuned for pavement crack detection is introduced.

76 0

Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN

Janpreet Singh, Shashank Shekhar•Sun Nov 11 2018

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.

35 0

Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis

Vishal Mandal, Abdul Rashid Mussah, Y. Adu-Gyamfi•Tue Oct 20 2020

Automatic detection and classification of pavement distresses is critical in timely maintaining and rehabilitating pavement surfaces. With the evolution of deep learning and high performance computing, the feasibility of vision-based pavement defect assessments has significantly improved. In this study, the authors deploy state-of-the-art deep learning algorithms based on different network backbones to detect and characterize pavement distresses. The influence of different backbone models such as CSPDarknet53, Hourglass-104 and EfficientNet were studied to evaluate their classification performance. The models were trained using 21,041 images captured across urban and rural streets of Japan, Czech Republic and India. Finally, the models were assessed based on their ability to predict and classify distresses, and tested using F1 score obtained from the statistical precision and recall values. The best performing model achieved an F1 score of 0.58 and 0.57 on two test datasets released by the IEEE Global Road Damage Detection Challenge. The source code including the trained models are made available at [1].

66 0

Road Damage Detection Based on Unsupervised Disparity Map Segmentation

Rui Fan, Ming Liu•Thu Oct 10 2019

A novel road damage detection algorithm based on unsupervised disparity map segmentation that requires no parameters when detecting road damage and performs both accurately and efficiently.

99 0

FasterRCNN Monitoring of Road Damages: Competition and Deployment

T. Hascoet, Y. Ariki, T. Takiguchi, Yihao Zhang, Andreas Persch, R. Takashima•Wed Oct 21 2020

A solution to the IEEE 2020 global Road Damage Detection (RDD) Challenge is detailed, and the efforts in deploying the model on a local road network are presented, explaining the proposed methodology and encountered challenges.

26 0

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

Vung V. Pham, Chau Pham, Tommy Dang•Tue Oct 27 2020

The results show that the X101-FPN base model for Faster R-CNN with Detectron2’s default configurations is efficient and general enough to be transferable to different countries in this challenge.

112 0

Road Damage Detection using Deep Ensemble Learning

Keval Doshi, Yasin Yılmaz•Thu Oct 29 2020

An ensemble model for efficient detection and classification of road damages, which has been submitted to the IEEE BigData Cup Challenge 2020 and utilizes a state-of-the-art object detector known as You Only Look Once (YOLO-v4), which is trained on images of various types of road damaged.

61 0

CNN Model & Tuning for Global Road Damage Detection

Rahul Vishwakarma, Ravigopal Vennelakanti•Wed Dec 09 2020

The effect of training on a per country basis with respect to a single generalizable model is studied and shows the generalizability of the Resnet-50 model when compared to its more complex counterparts.

34 0

Adding a benchmark result helps the community track progress.

Road Damage Detection | State-of-the-Art