The perception module plays a crucial role in the autonomous driving system, which is a very complicated system. In contrast, current perception research in the area of autonomous driving primarily focuses on the recognition of vehicles, lanes, and traffic signs, ignoring other factors that may cause traffic accidents, and fails to consider the fact that many traffic accidents on highways are caused by wild or wandering caused by animals. We conducted the following studies to close this gap in knowledge: First, a dataset of 1050 photos for huge animals that could appear on highways was created. Next, we propose a more efficient Yolo model by improving its backbone, replacing the C3 module with C3Ghost. The number of parameters is decreased to fewer than 3.7 million, just 52.7% of Yolov5s, but the average accuracy for each type of animals (mAP% 0.5) has reached over 95%. Third, our GhostSort- YoloNet (GS-YoloNet) also incorporates the Deep Sort algorithm to achieve real-time ranging and speed assessment of numerous targets, which has a very big practical application promise.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Real-Time Large Animal Detection Lightweight Network for Autonomous Driving on Highways


    Contributors:


    Publication date :

    2023-10-27


    Size :

    5376323 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Large animal detection and continuous traffic monitoring on highways

    Mukherjee, A. / Stolpner, S. / Liu, X. et al. | IEEE | 2013




    Real-Time Pedestrian Detection for Autonomous Driving

    Yang, Zhiheng / Li, Jun / Li, Huiyun | IEEE | 2018