Traffic sign detection is an important component of autonomous vehicles. There is still a mismatch problem between the existing detection algorithm and its practical application in real traffic scenes, which is mainly due to the detection accuracy and data acquisition. To tackle this problem, this study proposed an improved sparse R-CNN that integrates coordinate attention block with ResNeSt and builds a feature pyramid to modify the backbone, which enables the extracted features to focus on important information, and improves the detection accuracy. In order to obtain more diverse data, the augmentation method used is specifically designed for complex traffic scenarios, and we also present a traffic sign dataset in this study. For on-road autonomous vehicles, we designed two modules, self-adaption augmentation (SAA) and detection time augmentation (DTA), to improve the robustness of the detection algorithm. The evaluations on traffic sign datasets and on-road testing demonstrate the accuracy and effectiveness of the proposed method.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles


    Beteiligte:
    Tianjiao Liang (Autor:in) / Hong Bao (Autor:in) / Weiguo Pan (Autor:in) / Feng Pan (Autor:in)


    Erscheinungsdatum :

    2022




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Traffic Sign Detection using Deep Learning Techniques in Autonomous Vehicles

    Juyal, Amit / Sharma, Sachin / Matta, Priya | IEEE | 2021


    Traffic Sign Recognition using Image Processing for Autonomous Vehicles

    Devi, P. Dharani / A, Nawfal. / A, Devi. et al. | IEEE | 2023


    Traffic Sign Recognition Robustness in Autonomous Vehicles Under Physical Adversarial Attacks

    Apostolidis, Kyriakos D. / Gkouvrikos, Emmanouil V. / Vrochidou, Eleni et al. | Springer Verlag | 2023


    Sign Recognition for Autonomous Vehicles

    LIU DONGRAN / JAIN JINESH J | Europäisches Patentamt | 2018

    Freier Zugriff