Recognizing buses and other public transport vehicles can be a difficult task, especially in crowded and noisy environments. The primary objective of this research is to develop a robust and efficient system that can accurately detect bus routes and assist people in navigating public transportation. Our proposed system integrates a convolutional neural network-based deep learning model with optical character recognition technologies to detect bus routes efficiently in real-world scenarios. We employed YOLOv5 for object detection, which provides enhanced efficiency and accuracy in identifying buses and their routes. Additionally, we used PaddleOCR for precise character recognition to extract textual information from images, such as route numbers and destination names. By combining YOLOv5 and PaddleOCR, we have created a system that significantly improves the detection and recognition of bus routes compared to other competitive models. Our results demonstrate that the integrated approach not only enhances detection accuracy but also proves the efficacy of our model in practical applications. This research contributes to the development of intelligent transportation systems, making public transit more accessible and user-friendly for everyone.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Bus Route Detection Using YOLOv5 and PaddleOCR


    Weitere Titelangaben:

    CogScienceTechnology


    Beteiligte:
    Kumar, Amit (Herausgeber:in) / Ghinea, Gheorghita (Herausgeber:in) / Merugu, Suresh (Herausgeber:in) / Komal Adithya Reddy, B. (Autor:in) / Sohail, Mohammad (Autor:in) / Kolla, Morarjee (Autor:in)

    Kongress:

    International Conference on Information and Management Engineering ; 2023 ; Hyderabad, India December 08, 2023 - December 09, 2023



    Erscheinungsdatum :

    25.03.2025


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Military Aircraft Detection Using YOLOv5

    Ajay Kumar Goud, P. / Mohit Raj, G. / Rahul, K. et al. | Springer Verlag | 2023


    STD-Yolov5: a ship-type detection model based on improved Yolov5

    Ning, Yue / Zhao, Lining / Zhang, Can et al. | Taylor & Francis Verlag | 2024


    Detection of Driver Distraction using YOLOv5 Network

    Atas, Kubilay / Vural, Revna Acar | IEEE | 2021


    Real-Time Military Aircraft Detection using YOLOv5

    Rishi, Gudimetla Hanumath / Ramesh Kumar, P / Varshit, Namburu Chaitanya et al. | IEEE | 2024


    Pedestrian Detection Using YOLOv5 For Autonomous Driving Applications

    Vikram Reddy, Etikala Raja / Thale, Sushil | IEEE | 2021