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Cited by 37Infrastructure Maintenance and MonitoringAdvanced Neural Network ApplicationsPrivacy-Preserving Technologies in Data

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Federated learning–based global road damage detection

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Saha, Poonam Kumari, Arya, Deeksha, Sekimoto, Yoshihide·2024·Computer-Aided Civil and Infrastructure Engineering
DOI 10.1111/mice.13186
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Abstract

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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Metadata

Authors
Saha, P. K., Arya, D., & Sekimoto, Y.
Affiliations
  • The University of Tokyo (JP)
  • Center for Spatial Information Science, The University of Tokyo (JP)
Corresponding author
Arya, Deeksha
ORCID
Venue
Computer-Aided Civil and Infrastructure Engineering
Publisher
Elsevier BV
Published
2024-07
Volume
39
Issue
14
Pages
2223-2238
ISSN
1093-9687 (ISSN-L)
DOI
10.1111/mice.13186
OpenAlex
W4392474111
Cited by
37
References
5
Citations by year
2026: 15 · 2025: 14 · 2024: 8
FWCI
6.19
Type
journal article
Language
en
Open access
Licence
Locations
Computer-Aided Civil and Infrastructure Engineering (publishedVersion, cc-by)
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Keywords
federated learning, road damage detection, YOLOv5, object detection, data privacy, infrastructure inspection

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