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Toward EffectiveTraffic SignDetection viaTwo-Stage FusionNeural Networks Zhishan LiHongxu ChenBattista Biggio2024 год

Toward Effective Traffic Sign Detection via Two-Stage Fusion Neural Networks
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Аннотация: Automatic detection of traffic signs is crucial for Advanced Driving Assistance Systems (ADAS). Current two-stage approaches consist of a preliminary object detection step, where the traffic signs are categorized within broader families (e.g., speed limits), and then sub-classes (e.g., speed limit 40). However, these cascading methods fail to achieve satisfying performance, especially in more realistic driving scenarios where images are acquired under more challenging conditions. Under such conditions, the first-stage detection step is likely to provide inaccurate predictions, making the subsequent classification step useless. In this paper, we propose a simple yet effective two-stage fusion framework for traffic sign detection. Different from the previous cascading method, our framework directly predicts categories in the first-stage detection and fuse the two-stage category predictions to improves overall robustness. Besides, in order to filter the false detection boxes under low-resolution inputs, we also propose an effective post-processing method called Surrounding-Aware Non-Maximum Suppression (SA-NMS) as an alternative technique for the first-stage detection. After combining the above proposed methods, our framework obtains good detection performance. Experimental results on the widely used Tsinghua-Tencent 100K (TT100K) traffic sign dataset, which contains images of traffic signs collected under a variety of challenging conditions, show that the proposed framework outperforms current approaches in both accuracy and inference speed, achieving 89.7 mAP and 65 FPS for ${608\times608}$ low resolution images.
Год издания: 2024
Авторы: Zhishan Li, Hongxu Chen, Battista Biggio, Yifan He, Haoran Cai, Fabio Roli, Lei Xie
Издательство: Institute of Electrical and Electronics Engineers
Источник: IEEE Transactions on Intelligent Transportation Systems
Ключевые слова: Vehicle License Plate Recognition, Handwritten Text Recognition Techniques, Infrastructure Maintenance and Monitoring
Другие ссылки: IEEE Transactions on Intelligent Transportation Systems (HTML)
UNICA IRIS Institutional Research Information System (University of Cagliari) (PDF)
UNICA IRIS Institutional Research Information System (University of Cagliari) (HTML)