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 ${608\times 608}$ low resolution images.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Toward Effective Traffic Sign Detection via Two-Stage Fusion Neural Networks


    Contributors:
    Li, Zhishan (author) / Chen, Hongxu (author) / Biggio, Battista (author) / He, Yifan (author) / Cai, Haoran (author) / Roli, Fabio (author) / Xie, Lei (author)


    Publication date :

    2024-08-01


    Size :

    3233637 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Investigating Binary Neural Networks for Traffic Sign Detection and Recognition

    Chen, Ee Heng / Vemparala, Manoj Rohit / Fasfous, Nael et al. | IEEE | 2021


    INVESTIGATING BINARY NEURAL NETWORKS FOR TRAFFIC SIGN DETECTION AND RECOGNITION

    Chen, Ee Heng / Vemparala, Manoj Rohit / Fasfous, Nael et al. | British Library Conference Proceedings | 2021


    Convolutional Neural Networks for Traffic Sign Recognition

    Wei, Zhonghua / Gu, Heng / Zhang, Ran et al. | TIBKAT | 2021


    Traffic sign recognition using convolutional neural networks

    Boujemaa, Kaoutar Sefrioui / Bouhoute, Afaf / Boubouh, Karim et al. | IEEE | 2017


    Traffic Sign Detection in the Digital Era: Leveraging Convolutional Neural Networks

    G, Kothai / Anbumozhi, Anna / R, Abirami et al. | IEEE | 2023