FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications

IF 4.6 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Open Journal of Intelligent Transportation Systems Pub Date : 2024-07-22 DOI:10.1109/OJITS.2024.3432176

Ioannis V. Vondikakis;Ilias E. Panagiotopoulos;George J. Dimitrakopoulos

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引用次数: 0

Abstract

The state of road surfaces can have a significant impact on vehicle handling, passenger comfort, safety, fuel consumption, and maintenance requirements. For this reason, it is important to analyze road conditions in order to improve traffic safety, optimize fuel efficiency, and provide a smoother travel experience. This research presents a federated learning analysis that brings together edge computing and cloud technology, by identifying various road conditions through a multi-label road surface classification analysis. The presented analysis prioritizes the privacy of road users’ data and leverages the advantages of collective data analysis while building confidence in the system. Multi-label classification is applied in order to capture complexity by assigning multiple relevant labels, thus providing a richer and more detailed understanding of the road conditions. According to the experiments, this approach efficient classifies road surface images, achieving comprehensive coverage even in scenarios where data from certain edges is limited.

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FedRSC：针对多标签路面分类的联合学习分析

路面状况会对车辆操控性、乘客舒适度、安全性、油耗和维护要求产生重大影响。因此，为了提高交通安全、优化燃油效率并提供更顺畅的出行体验，对路面状况进行分析非常重要。本研究提出了一种联合学习分析方法，将边缘计算和云技术结合起来，通过多标签路面分类分析来识别各种路况。本分析报告优先考虑了道路用户数据的隐私性，并充分利用了集体数据分析的优势，同时建立了对系统的信心。采用多标签分类是为了通过分配多个相关标签来捕捉复杂性，从而提供对路况更丰富、更详细的了解。实验结果表明，这种方法能有效地对路面图像进行分类，即使在某些边缘数据有限的情况下也能实现全面覆盖。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Open Journal of Intelligent Transportation Systems

CiteScore

5.40

自引率

0.00%

发文量