The detection of traffic signs in clean and noise-free images has been investigated by numerous researchers; however, very few of these works have focused on noisy environments. While in the real world, for different reasons (e.g. the speed and acceleration of a vehicle and the roughness around it), the input images of the convolutional neural networks (CNNs) could be extremely noisy. Contrary to other research works, in this paper, we investigate the robustness of the deep learning models against the synthetically modeled noises in the detection of small objects. To this end, the state-of-the-art architectures of Faster-RCNN Resnet101, R-FCN Resnet101, and Faster-RCNN Inception Resnet V2 are trained by means of the Tsinghua-Tencent 100K database, and the performances of the trained models on noisy data are evaluated. After verifying the robustness of these models, different training scenarios (1 – Modeling various climatic conditions, 2 – Style randomization, and 3 – Augmix augmentation) are used to enhance the model robustness. The findings indicate that these scenarios result in up to 13.09%, 12%, and 13.61% gains in the mentioned three networks by means of the mPC metric. They also result in 11.74%, 8.89%, and 7.27% gains in the rPC metric, demonstrating that improvement in robustness does not lead to performance drop on the clean data.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Enhancing the robustness of the convolutional neural networks for traffic sign detection


    Contributors:


    Publication date :

    2022-07-01


    Size :

    13 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Enhancing the Traffic Sign Detection to Improve Convolutional Neural Network

    Sungheetha, Akey / V, Vijeya kaveri / V, Sruthi Sri et al. | IEEE | 2024


    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


    Real-Time Traffic Sign Recognition Using Convolutional Neural Networks

    Rao, Aditya / Motwani, Rahul / Sarguroh, Naveed et al. | Springer Verlag | 2021