The number of new vehicles on the road is increasing rapidly, which in turn causes highly congested roads and serving as a reason to break traffic rules by violating them. This leads to a high number of road accidents. Traffic violation detection systems using computer vision are a very efficient tool to reduce traffic violations by tracking and Penalizing. The proposed system was implemented using YOLOV3 object detection for traffic violation detections such as signal jump, vehicle speed, and the number of vehicles. Further, the system is optimized in terms of accuracy. Using the Region of interest and location of the vehicle in the duration of frames, determining signal jump. This implementation obtained an accuracy of 97.67% for vehicle count detection and an accuracy of 89.24% for speed violation detection.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic Signal Violation Detection using Artificial Intelligence and Deep Learning


    Beteiligte:
    Franklin, Ruben J (Autor:in) / Mohana (Autor:in)


    Erscheinungsdatum :

    2020-06-01


    Format / Umfang :

    655740 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Traffic Rules Violation Detection using Deep Learning

    Tonge, Aniruddha / Chandak, Shashank / Khiste, Renuka et al. | IEEE | 2020


    Traffic Rule Violation Detection System: Deep Learning Approach

    Kathane, Mandar / Abhang, Shubham / Jadhavar, Abhishek et al. | Springer Verlag | 2022


    Traffic Signal Violation Detection System using YOLOv3

    Sinha, Dipali / Divya, S. / Anjali, C. et al. | IEEE | 2024


    Intersection Traffic Signal Utilizing Artificial Intelligence

    LIM YOUNG HAN / KIM HAE JONG | Europäisches Patentamt | 2021

    Freier Zugriff

    Intersection Traffic Signal Utilizing Artificial Intelligence

    Europäisches Patentamt | 2022

    Freier Zugriff