Toward Safe Driving: Efficient Detection of Small Blurred Signs in Real-World Scenarios

Accurate traffic sign recognition is critical for safe driving, as over half of traffic accidents stem from drivers’ negligence of traffic signs. Thus, developing robust traffic sign detection methods is essential to improve road safety. While existing object detection methods have achieved remarkable success, their performance in traffic sign detection is often limited by small object sizes and low-resolution appearances. To address these issues, this study proposes a novel traffic sign detection framework with three innovative components for precise localization and classification: 1) a hierarchical feature aggregation module that emphasizes high-level semantic information for traffic sign localization; 2) a cross-layer semantic residual network that enhances recognition of small and blurred signs via hierarchical feature interaction and fusion; 3) a lightweight feature alignment unit that bridges semantic gaps between cross-level representations. These components jointly tackle the challenges of detecting small and blurred traffic signs in real-world driving scenarios. Experiments were conducted on three public datasets (TT100K, CCTSDB2021, GTSDB). Results show the proposed model outperforms other methods with comparable parameters. Additionally, dynamic motion blur augmentation was applied to datasets to simulate real driving scenarios, and experiments confirm the proposed method achieves state-of-the-art performance under such challenging conditions. Code is publicly available athttps://github.com/Mo7nex/SAttFusion-YOLO