Road Rutting Detection using Deep Learning on Images

Saha, Poonam Kumari; Arya, Deeksha; Kumar, Ashutosh; Maeda, Hiroya; Sekimoto, Yoshihide

doi:10.1109/bigdata55660.2022.10020458

Cited by 6 publications

(1 citation statement)

References 28 publications

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“…Recent research uses image processing (Bello‐Salau et al., 2014; Rodriguez‐Lozano et al., 2023, etc. ), image segmentation methods (Liu et al., 2020; Saha et al., 2022; Siriborvornratanakul, 2023, etc. ), and deep learning methods (Arya et al., 2021; Li et al., 2021; Maeda et al., 2018, etc.)…”

Section: Introductionmentioning

confidence: 99%

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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