Road Damage Detection for Multiple Countries

Saha, Poonam Kumari; Sekimoto, Yoshihide

doi:10.1109/bigdata55660.2022.10020619

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…The reason for the similarity in performance obtained by Japan and the United States could be because both the Japan and US data set is made of good‐quality data. However, the images of India data are not of good quality (Saha & Sekimoto, 2022) because of poor image capturing due to dust, sun glare, motion blur, and pollution.…”

Section: Discussionmentioning

confidence: 99%

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Federated learning–based global road damage detection

Saha,

Arya,

Sekimoto

2024

Computer aided Civil Eng

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Deep learning is widely used for road damage detection, but it requires extensive, diverse, and well‐labeled data. Centralized model training can be difficult due to large data transfers, storage needs, and computational resources. Data privacy concerns can also hinder data sharing among clients, leaving them to train models on their own data, leading to less robust models. Federated learning (FL) addresses these problems by training models without data sharing, only exchanging model parameters between clients and the server. This study deploys FL along with YOLOv5l to generate models for single‐ and multi‐country applications. These models gave 21%–25% lesser mean average precision (mAP) than centralized models but outperformed local client models by 1.33%–163% on the global test data.

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Section: Discussionmentioning

confidence: 99%

“…This data set was named as multicountry road damage data set (MRDD). While using the RDD2022 data set, only positive images (Saha & Sekimoto, 2022) were considered. By positive images, it means that only those images that had at least one instance of road damage type in them.…”

Section: Methodsmentioning

confidence: 99%

Federated learning–based global road damage detection

Saha,

Arya,

Sekimoto

2024

Computer aided Civil Eng

Self Cite

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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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Road Damage Detection for Multiple Countries

Cited by 2 publications

References 16 publications

Federated learning–based global road damage detection

Federated learning–based global road damage detection

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

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