Object detection is the most critical and foundational sensing module for the autonomous movement platform. However, most of the existing deep learning solutions are based on GPU servers, which limits their actual deployment. We present an efficient multi-sensor fusion based object detection model that can be deployed on the off-the-shelf edge computing device for the vehicle platform. To achieve real-time target detection, the model eliminates a large number of invalid point clouds through ground filtering algorithm, and then adds texture information (fused from camera image) through point cloud coloring to enhance features. The proposed PV-EncoNet efficiently encodes both the spatial and texture features of each colored point through point-wise and voxel-wise encoding, and then predicts the position, heading and class of the objects. The final model can achieve about 17.92 and 24.25 Frame per Second (FPS) on two different edge computing platforms, and the detection accuracy is comparable with the state-of-the-art models on the KITTI public dataset (i.e., 88.54% for cars, 71.94% for pedestrians and 73.04% for cyclists). The robustness and generalization ability of the PV-EncoNet for the 3D colored point cloud detection task is also verified by deploying it on the local vehicle platform and testing it on real road conditions.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    PV-EncoNet: Fast Object Detection Based on Colored Point Cloud


    Beteiligte:
    Ouyang, Zhenchao (Autor:in) / Dong, Xiaoyun (Autor:in) / Cui, Jiahe (Autor:in) / Niu, Jianwei (Autor:in) / Guizani, Mohsen (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.08.2022


    Format / Umfang :

    3318530 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Covariance based point cloud descriptors for object detection and recognition

    Fehr, Duc / Beksi, William J. / Zermas, Dimitris et al. | British Library Online Contents | 2016


    Pre-pruned Distillation for Point Cloud-based 3D Object Detection

    Li, Fuyang / Min, Chen / Xiao, Liang et al. | IEEE | 2024


    Multi-view 3D Object Detection Based on Point Cloud Enhancement

    Chen, Shijie / Wang, Wei | Springer Verlag | 2022


    Covariance based point cloud descriptors for object detection and recognition

    Fehr, Duc / Beksi, William J. / Zermas, Dimitris et al. | British Library Online Contents | 2016


    Covariance based point cloud descriptors for object detection and recognition

    Fehr, Duc / Beksi, William J. / Zermas, Dimitris et al. | British Library Online Contents | 2016