In this research, we address the challenge of detecting vehicles in inclement weather using a YOLO-based algorithm. By applying CNNs, we intend to improve vehicle recognition in difficult conditions including fog and rain, enhancing the safety as well as efficiency of autonomous vehicles. This research lays the groundwork for robust perception systems that can confidently navigate challenging weather scenarios.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Improving Object Detection and Classification for Autonomous Vehicle in Adverse Weather Conditions




    Publication date :

    2024-12-06


    Size :

    947756 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Object Detection of Autonomous Vehicles under Adverse Weather Conditions

    Arthi, V. / Murugeswari, R. / P, Nagaraj | IEEE | 2022


    Object Detection and Tracking for Autonomous Vehicles in Adverse Weather Conditions

    Bhadoriya, Abhay Singh / Vegamoor, Vamsi Krishna / Rathinam, Sivakumar | British Library Conference Proceedings | 2021


    Object Detection and Tracking for Autonomous Vehicles in Adverse Weather Conditions

    Bhadoriya, Abhay Singh / Rathinam, Sivakumar / Vegamoor, Vamsi Krishna | SAE Technical Papers | 2021


    Determination of Changes in Autonomous Vehicle Location Under Adverse Weather Conditions

    KAMINITZ YAAKOV | European Patent Office | 2023

    Free access

    Object Detection System in Adverse Weather Conditions Using Ann

    Joshua, Jeberson A / Narasiman, L / Yogeshwaran, V et al. | IEEE | 2023