Self-driving cars are an active area of interdisciplinary research spanning Artificial Intelligence (AI), Internet of Things (IoT), embedded systems, and control engineering. One crucial component needed in ensuring autonomous navigation is to accurately detect vehicles, pedestrians, or other obstacles on the road and ascertain their distance from the self-driving vehicle. The primary algorithms employed for this purpose involve the use of cameras and Light Detection and Ranging (LiDAR) data. Another category of algorithms consists of a fusion between these two sensor data. Sensor fusion networks take input as 2D camera images and LiDAR point clouds to output 3D bounding boxes as detection results. In this paper, we experimentally evaluate the performance of three object detection methods based on the input data type. We offer a comparison of three object detection networks by considering the following metrics - accuracy, performance in occluded environment, and computational complexity. YOLOv3, BEV network, and Point Fusion were trained and tested on the KITTI benchmark dataset. The performance of a sensor fusion network was shown to be superior to single-input networks. Full Text


    Access

    Download


    Export, share and cite



    Title :

    A Comparative Analysis of Camera, LiDAR and Fusion Based Deep Neural Networks for Vehicle Detection


    Contributors:

    Publication date :

    2022-01-29


    Remarks:

    International Journal of Innovations in Science & Technology; Vol. 3 No. 4 (2021): Special_Issue NUML; 177-186 ; 2709-6130 ; 2618-1630


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




    Deep fusion of camera and LIDAR

    Schneider, Nick | TIBKAT | 2023

    Free access

    Deep Learning-based Radar, Camera, and Lidar Fusion for Object Detection

    Nobis, Felix Otto Geronimo | TIBKAT | 2022

    Free access

    Online vehicle detection using deep neural networks and lidar based preselected image patches

    Lange, Stefan / Ulbrich, Fritz / Goehring, Daniel | IEEE | 2016


    Robust Camera Lidar Sensor Fusion Via Deep Gated Information Fusion Network

    Kim, Jaekyum / Choi, Jaehyung / Kim, Yechol et al. | IEEE | 2018


    LIDAR AND CAMERA DATA FUSION FOR AUTOMATED VEHICLE

    WEI JUNQING / KIM JUNSUNG / LEE JONG HO | European Patent Office | 2018

    Free access