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.


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    Title :

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


    Contributors:


    Publication date :

    2025-05-29


    Size :

    3155397 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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