Rapid urbanization has led to a variety of problems in road traffic, such as recurrent congestion and traffic accidents. To mitigate these problems, it is necessary to monitor and analyze traffic volumes in order to anticipate or detect anomalies in road traffic. This work proposes a solution for traffic accident scene recognition using computer vision applied to drone imagery. A Mavic Air 2S drone collects geo-referenced aerial images of the portion of road to be monitored, and the data is annotated, trained and tested on Roboflow. The results of the experiment give an overall object detection rate of 95.3%, accuracy of 94.4% and recall of 91.3%. Geographical coordinates of images containing accident scenes are extracted and stored in a database for statistical and rescue purposes.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Real-Time Road Accident Scene Recognition Using Computer Vision Applied to Drone Imagery




    Publication date :

    2024-11-08


    Size :

    724951 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery

    Fonod, Robert / Cho, Haechan / Yeo, Hwasoo et al. | ArXiv | 2024

    Free access


    Measuring drone flight-stability using computer vision

    Dezső; Dávid / Sarvajcz Kornél | DOAJ | 2018

    Free access

    Real-time road scene classification using infrared images

    Forslund, David / Cronvall, Per / Roll, Jacob | Tema Archive | 2010


    Vision based rail track extraction and monitoring through drone imagery

    Arun Kumar Singh / Anushka Swarup / Ankush Agarwal et al. | DOAJ | 2019

    Free access