FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications
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 tr...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10606293/ |
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| author | Ioannis V. Vondikakis Ilias E. Panagiotopoulos George J. Dimitrakopoulos |
| author_facet | Ioannis V. Vondikakis Ilias E. Panagiotopoulos George J. Dimitrakopoulos |
| author_sort | Ioannis V. Vondikakis |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e391e2f28162417ea7cfd9da06ca0cb7 |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-e391e2f28162417ea7cfd9da06ca0cb72025-01-24T00:02:42ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-01543344410.1109/OJITS.2024.343217610606293FedRSC: A Federated Learning Analysis for Multi-Label Road Surface ClassificationsIoannis V. Vondikakis0https://orcid.org/0009-0005-5451-4183Ilias E. Panagiotopoulos1https://orcid.org/0000-0003-4366-6470George J. Dimitrakopoulos2https://orcid.org/0000-0002-7424-8557Department of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceThe 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.https://ieeexplore.ieee.org/document/10606293/Machine learningconvolutional neural networkfederated learningroad surface analysis |
| spellingShingle | Ioannis V. Vondikakis Ilias E. Panagiotopoulos George J. Dimitrakopoulos FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications IEEE Open Journal of Intelligent Transportation Systems Machine learning convolutional neural network federated learning road surface analysis |
| title | FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications |
| title_full | FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications |
| title_fullStr | FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications |
| title_full_unstemmed | FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications |
| title_short | FedRSC: A Federated Learning Analysis for Multi-Label Road Surface Classifications |
| title_sort | fedrsc a federated learning analysis for multi label road surface classifications |
| topic | Machine learning convolutional neural network federated learning road surface analysis |
| url | https://ieeexplore.ieee.org/document/10606293/ |
| work_keys_str_mv | AT ioannisvvondikakis fedrscafederatedlearninganalysisformultilabelroadsurfaceclassifications AT iliasepanagiotopoulos fedrscafederatedlearninganalysisformultilabelroadsurfaceclassifications AT georgejdimitrakopoulos fedrscafederatedlearninganalysisformultilabelroadsurfaceclassifications |