Autonomous vehicles use a variety of software-based detection algorithms to detect the static and dynamic objects in their surroundings. Detected objects are then used as bases for the higher-level tasks required for the safe operation of the vehicles. Due to their abundance in most road environments, poles are some of the key static landmarks used for the tasks of autonomous vehicle mapping and localization. However, most of the work done in the field of pole-like object detection has been focused on the use of LiDAR-generated 3D point clouds. This study explores the validity of monocular camera-based pole detection approaches as means of redundancy for environmental perception. It uses the YOLOv4 object detector combined with the MiDaS depth estimator to ultimately produce pole detections on inverse depth maps. To train and test the YOLOv4 model, a new 3000-image dataset based on BDD100K was created by manually generating accurate pole ground truth labels. The final model produced by the study is capable of predicting poles with an mAP of over 71%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Detection of Vertical Poles in a Road Environment Using Monocular Images based on YOLOv4


    Contributors:


    Publication date :

    2022-11-14


    Size :

    718867 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Vehicle Detection System using YOLOv4

    Vashishtha, Srishti / Kumar, Suraj / Bothra, Vishakha et al. | IEEE | 2022


    Automated Drone Detection Using YOLOv4

    Subroto Singha / Burchan Aydin | DOAJ | 2021

    Free access

    High-performance on-road vehicle detection in monocular images

    Gabb, Michael / Lohlein, Otto / Wagner, Raimar et al. | IEEE | 2013


    Improved YOLOv4 for Pedestrian Detection and Counting in UAV Images

    Hao Kong / Zhi Chen / Wenjing Yue et al. | DOAJ | 2022

    Free access

    Pedestrian Detection Algorithm Based on Improved YOLOv4

    Zheng, Ziheng / Ni, Chenhui / Zeng, Guolei | IEEE | 2023