Traffic Sign Recognition in Rainy Conditions Based on Federated Learning
Yilin Chen · ITM Web of Conferences · 2025
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.