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| Title | Enhanced Road Damage Detection With Federated Learning Across Diverse And Heterogeneous Global Datasets |
|---|---|
| ID_Doc | 23667 |
| Authors | Dwivedi S.K.; Arya D.; Sekimoto Y. |
| Year | 2024 |
| Published | 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024 |
| DOI | http://dx.doi.org/10.1145/3678717.3695758 |
| Abstract | There is an urgent need for innovative technologies to detect road damage efficiently and cost-effectively. Traditional centralized deep learning models face challenges due to extensive data transfer and privacy concerns when sharing data among different parties. Federated Learning (FL) addresses these issues by sharing model parameters instead of raw data, enhancing collaboration without compromising privacy. While previous studies focused on similar datasets from various countries, this research showcases FL's efficacy with diverse data from Japan, China, and Norway. Our proposed solution involves developing a multi-country federated model capable of learning from diverse datasets and enhancing road damage detection accuracy across different regions, making it more robust and generalized than traditional models. Using YOLOv8l and the Flower framework with the FedAvg strategy, the FL model achieved an mAP50 of 0.467 on a multi-country test dataset, outperforming Japan's centralized model by 3.6% and Norway's and China's models by over 20%. © 2024 Copyright is held by the owner/author(s). |
| Author Keywords | Automation; Big Data; Deep Learning; Federated Learning (FL); Global Road Damage Detection; Smart City Applications |
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