Rail transport is considered one of the safest modes of transport. Along with the development of technology, significant developments have been observed in railway transportation over the years. With the increasing railway line, the demand for railway transportation is increasing day by day. The number of passengers using railway transportation has also increased in this context. With this intensity, the damage to the railway line increases. In ensuring the safety of railway transport, methods based on deep learning have become important to ensure railway safety. In order for the railway line to provide healthy service, the monitoring of the railway line should be done regularly. Traditional rail monitoring services require different vehicles. Today, besides conventional vehicles, drones are used for the monitoring of the railway line. Experimenting with drones in the real environment can be difficult and costly. It is always more advantageous to run the codes that will run on the drone first in a simulation environment to save time. In this study, a drone-based system that autonomously tracks and detects railway tracks is proposed as an alternative to traditional methods. The proposed method detects the rails with the semantic segmentation method and follows the rails with its front camera. The proposed method was developed in the Gazebo environment. The general purpose of the study is to record the rail images with the drone camera that follows the railway autonomously. In this way, drone experiments to be carried out in the real railway environment will be completed in a shorter time.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Rail Tracking and Detection with Drone in Gazebo Environment


    Beteiligte:
    Sevi, Mehmet (Autor:in) / Aydin, Ilhan (Autor:in)


    Erscheinungsdatum :

    23.03.2022


    Format / Umfang :

    1153646 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    ROS2-Gazebo Simulator for Drone Applications

    Haridevan, Amal Dev / Kang, Junjie / Yuan, Mingfeng et al. | IEEE | 2024


    Drone Stability Simulation Using ROS and Gazebo

    Megalingam, Rajesh Kannan / Prithvi, Darla Vineeth / Kumar, Nimmala Chaitanya Sai et al. | Springer Verlag | 2021


    Real-Time Drone System for Detecting, Tracking, and Following of a Mobile Robot in ROS/Gazebo

    Salman, Ali / Dika, Mohammad / Diab, Hassan et al. | IEEE | 2025


    GAZEBO-BASED SIMULATION ENVIRONMENT INTEGRATION FOR UAS

    Guo, Aaron / Hu, Hanyao / Wang, Linzhe et al. | TIBKAT | 2021


    Gazebo-Based Simulation Environment Integration for UAS

    Guo, Aaron / Hu, Hanyao / Wang, Linzhe et al. | AIAA | 2021