From global challenges to local solutions: A review of cross-country collaborations and winning strategies in road damage detection

Arya, Deeksha; Maeda, Hiroya; Sekimoto, Yoshihide

doi:10.1016/j.aei.2024.102388

Advanced Engineering Informatics

2024

DOI: 10.1016/j.aei.2024.102388

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From global challenges to local solutions: A review of cross-country collaborations and winning strategies in road damage detection

Deeksha Arya,

Hiroya Maeda,

Yoshihide Sekimoto

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2024

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Cited by 3 publications

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RDD2022: A multi‐national image dataset for automatic road damage detection

Arya,

Maeda,

Ghosh

et al. 2024

Geoscience Data Journal

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The data article describes the Road Damage Dataset, RDD2022, encompassing of 47,420 road images from majorly six countries, Japan, India, the Czech Republic, Norway, the United States, and China. The dataset incorporates over 55,000 instances of road damage, specifically longitudinal cracks, transverse cracks, alligator cracks, and potholes. Designed to facilitate the development of deep learning methodologies for automated road damage detection and classification, RDD2022 was unveiled as part of the Crowd sensing‐based Road Damage Detection Challenge (CRDDC'2022), with a major contribution from the challenge winners. This challenge garnered global participation, urging researchers to propose solutions for automatic road damage detection in multiple countries. A noteworthy outcome of CRDDC'2022 was the emergence of a top‐performing model achieving a remarkable F1 Score of 76.9% for road damage detection in all six countries using RDD2022. This success underscores the dataset's practical applicability for municipalities and road agencies, enabling low‐cost, automatic monitoring of road conditions. Beyond its immediate utility, RDD2022 stands as a valuable benchmark for researchers in computer vision, geoscience, and machine learning, offering a rich resource for algorithmic evaluation in diverse image‐based applications, including classification and object detection. The latest big data cup, Optimized Road Damage Detection Challenge (ORDDC'2024), is also based on RDD2022, underscoring its continued relevance and pivotal role in current research and development endeavors.

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RDD2022: A multi‐national image dataset for automatic road damage detection

Arya,

Maeda,

Ghosh

et al. 2024

Geoscience Data Journal

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Crack segmentation using discrete cosine transform in shadow environments

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Liu

2024

Automation in Construction

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Ma,

Tong

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From global challenges to local solutions: A review of cross-country collaborations and winning strategies in road damage detection

Cited by 3 publications

References 41 publications

RDD2022: A multi‐national image dataset for automatic road damage detection

RDD2022: A multi‐national image dataset for automatic road damage detection

Crack segmentation using discrete cosine transform in shadow environments

Deployment strategies for lightweight pavement defect detection using deep learning and inverse perspective mapping

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