A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Federated learning–based global road damage detection
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.
Federated learning–based global road damage detection
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.
Federated learning–based global road damage detection
Saha, Poonam Kumari (author) / Arya, Deeksha (author) / Sekimoto, Yoshihide (author)
Computer‐Aided Civil and Infrastructure Engineering ; 39 ; 2223-2238
2024-07-01
16 pages
Article (Journal)
Electronic Resource
English
| European Patent Office | 2023
ROAD SURFACE DAMAGE DETECTION SYSTEM, ROAD SURFACE DAMAGE DETECTION METHOD, PROGRAM
| European Patent Office | 2021
ROAD SURFACE DAMAGE DETECTION SYSTEM, ROAD SURFACE DAMAGE DETECTION METHOD, PROGRAM
| European Patent Office | 2021