Traffic sign boards are vital in facilitating smart transportation systems. More than 90% of accidents happen due to drivers’ inattentiveness over these boards. Hence, relaying traffic sign board information automatically to drivers becomes crucial to avoid such accidents. While numerous traffic sign detection and classification systems exist, it is important to note that these automated systems have not been adequately assessed within the context of Indian settings. The task of traffic sign detection and classification presents unique challenges in the Indian context due to the presence of multiple variations of signboards for a single action (e.g., left turn and right turn). This paper proposes a novel deep neural network architecture for detecting and classifying traffic signboards simultaneously. In the proposed architecture, several state-of-the-art convolutional neural networks such as AlexNet, VGG-19, ResNet-50, and EfficientNet v2 are used as a backbone to solve the task of bounding box regression and sign classification. Along with the proposed method, we collected the Indian traffic signs and information boards dataset. The collected dataset consists of 4257 raw images without any augmentation. We comprehensively evaluated the proposed algorithm on the collected dataset, resulting in the detection and classification accuracies of 85.5% and 98.5%, respectively. The proposed algorithm has also been evaluated on the existing traffic sign recognition dataset, and a comparison with state-of-the-art algorithms is done for both traffic sign detection and classification, demonstrating the effectiveness of the proposed multi-task network.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Indian Traffic Sign Detection and Classification Through a Unified Framework


    Beteiligte:
    Uikey, Rishabh (Autor:in) / Lone, Haroon R. (Autor:in) / Agarwal, Akshay (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.10.2024


    Format / Umfang :

    1954508 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Indian Traffic Sign Detection and Recognition

    Alam, Altaf / Jaffery, Zainul Abdin | Springer Verlag | 2020


    Traffic Sign Detection System: Indian Challenging Scenario

    R, Suresha / N, Manohar / P, Bhumika Jain et al. | IEEE | 2024


    Traffic Sign Detection and Classification

    Sharma, Vinayak / Kumar, Vimal / Aditya, Harshvardhan | IEEE | 2023


    Traffic sign detection model transmitted according to traffic sign classification information

    LU NAN / SHEN QIDONG / CAI XUFENG et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Road traffic sign detection and classification

    Escalera, A. de la / Moreno, L.E. / Salichs, M.A. et al. | Tema Archiv | 1997