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.

中文翻译：

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基于联邦学习的全局道路损伤检测

深度学习广泛用于道路损坏检测，但它需要广泛、多样化且标记良好的数据。由于大量数据传输、存储需求和计算资源，集中式模型训练可能很困难。数据隐私问题还可能阻碍客户之间的数据共享，使他们只能根据自己的数据训练模型，从而导致模型的稳健性较差。联邦学习（FL）通过在不共享数据的情况下训练模型，仅在客户端和服务器之间交换模型参数来解决这些问题。本研究部署 FL 和 YOLOv5l 来生成适用于单国和多国应用的模型。这些模型的平均精度 (mAP) 比集中式模型低 21%–25%，但在全球测试数据上比本地客户端模型高 1.33%–163%。