Traffic Sign Recognition in Rainy Conditions Based on Federated Learning
The challenge of traffic sign recognition in rainy conditions poses significant difficulties for autonomous driving systems, primarily due to obscured visibility and altered sign characteristics. To tackle this issue, this paper simulated rainy environments to improve the recognition accuracy of tra...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01017.pdf |
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| _version_ | 1825206572369838080 |
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| author | Chen Yilin |
| author_facet | Chen Yilin |
| author_sort | Chen Yilin |
| collection | DOAJ |
| description | The challenge of traffic sign recognition in rainy conditions poses significant difficulties for autonomous driving systems, primarily due to obscured visibility and altered sign characteristics. To tackle this issue, this paper simulated rainy environments to improve the recognition accuracy of traffic signs in real world. This paper utilized OpenCV to preprocess images by adding a rain effect, thereby enhancing the dataset’s realism. Subsequently, this study implemented a LeNet model within a Federated Learning framework, which enables decentralized training while preserving data privacy. The approach involved leveraging the Belgium Traffic Sign Classification Benchmark dataset, achieving an impressive accuracy of approximately 93% in recognizing traffic signs despite the simulated rainy conditions. The federated learning model effectively aggregated knowledge from multiple clients, resulting in a more resilient and efficient recognition system. The proposed method is demonstrated by experimental results to enhance performance in challenging weather conditions while also maintaining data privacy in machine learning applications. Overall, this paper underscores the potential of integrating federated learning with CNNs to improve traffic sign recognition capabilities. |
| format | Article |
| id | doaj-art-7fd2b7d423544a0fba656f8b56e34aac |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-7fd2b7d423544a0fba656f8b56e34aac2025-02-07T08:21:10ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700101710.1051/itmconf/20257001017itmconf_dai2024_01017Traffic Sign Recognition in Rainy Conditions Based on Federated LearningChen Yilin0Computer Science, Wuhan University of Science and TechnologyThe challenge of traffic sign recognition in rainy conditions poses significant difficulties for autonomous driving systems, primarily due to obscured visibility and altered sign characteristics. To tackle this issue, this paper simulated rainy environments to improve the recognition accuracy of traffic signs in real world. This paper utilized OpenCV to preprocess images by adding a rain effect, thereby enhancing the dataset’s realism. Subsequently, this study implemented a LeNet model within a Federated Learning framework, which enables decentralized training while preserving data privacy. The approach involved leveraging the Belgium Traffic Sign Classification Benchmark dataset, achieving an impressive accuracy of approximately 93% in recognizing traffic signs despite the simulated rainy conditions. The federated learning model effectively aggregated knowledge from multiple clients, resulting in a more resilient and efficient recognition system. The proposed method is demonstrated by experimental results to enhance performance in challenging weather conditions while also maintaining data privacy in machine learning applications. Overall, this paper underscores the potential of integrating federated learning with CNNs to improve traffic sign recognition capabilities.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01017.pdf |
| spellingShingle | Chen Yilin Traffic Sign Recognition in Rainy Conditions Based on Federated Learning ITM Web of Conferences |
| title | Traffic Sign Recognition in Rainy Conditions Based on Federated Learning |
| title_full | Traffic Sign Recognition in Rainy Conditions Based on Federated Learning |
| title_fullStr | Traffic Sign Recognition in Rainy Conditions Based on Federated Learning |
| title_full_unstemmed | Traffic Sign Recognition in Rainy Conditions Based on Federated Learning |
| title_short | Traffic Sign Recognition in Rainy Conditions Based on Federated Learning |
| title_sort | traffic sign recognition in rainy conditions based on federated learning |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_01017.pdf |
| work_keys_str_mv | AT chenyilin trafficsignrecognitioninrainyconditionsbasedonfederatedlearning |