The paper discussed the introduction of a YOLO-based AI system for the detection of road incidents in real-time as an effort toward enhanced road safety by automating the detection and response to road-related risks such as accidents and speeding vehicles. Using YOLOv8 and YOLO11x models trained on a broad dataset of 15,000 traffic scenario images, the system detects various types of accidents and vehicle speeds with high accuracy and efficiency under different conditions. The proposed solution addresses main challenges of existing surveillance systems through real-time processing, sensor-free speed estimation, and robustness against low-resolution inputs, thus proving to be a great promise for improving traffic management and safety.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    AI for Traffic Safety: Real-Time YOLO-Based System for Detecting Road Incidents


    Beteiligte:


    Erscheinungsdatum :

    29.05.2025


    Format / Umfang :

    3155397 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Real-time Detection of Road Traffic Incidents

    Pero Škorput / Sadko Mandžuka / Niko Jelušić | DOAJ | 2010

    Freier Zugriff

    Real Time Implementation of Computer Vision based Road Traffic Monitoring System using YOLO 11

    K, Purushothaman. / R.K., Ponginnan / R, Brindha et al. | IEEE | 2025


    Systems and methods for detecting road congestion and incidents in real time

    STENNETH LEON | Europäisches Patentamt | 2016

    Freier Zugriff


    Monitoring of traffic incidents in real time for decision making in road infrastructures

    de-la-Cruz-Nicolas, Ernesto / Martinez-Rebollar, Alicia / Estrada-Esquivel, Hugo et al. | IEEE | 2022