In recent years, due to its strong plasticity and excellent future potential, the development of automated vehicles in the mining environment is extraordinarily rapid. The application of lidar in automated vehicles has also become more and more popular, and algorithms for point cloud object detection have emerged endlessly. However, due to rough road and dusk-to-dawn operations in the mining scenario and the large size of the truck, traditional detection algorithms fail to meet the requirements of real-time updates. Due to the high precision and efficiency of the deep learning network, its application could break through the limitations of traditional algorithms. In this paper, the algorithm called PointPillars is adapted for object detection in the mining scenario. After converting the point cloud to a sparse pseudo-image and extracting features by a two-dimensional convolutional neural network (2D CNN), the time consumption of the entire algorithm becomes much less. The application of a Single-Shot Detector (SSD) detection head also improves the accuracy of the detection results. The mean Average Precision (mAP) is up to 50%, which is sufficient for application in the actual scenario. In addition, the algorithm accomplishes object detection in 69 ms, which can meet the speed requirement of lidar update and achieve the real-time application.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Three-Dimensional Object Detection Based on Deep Learning in Enclosed Scenario


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Yu, Guizhen (Autor:in) / Cui, Jieming (Autor:in) / Zhang, Na (Autor:in) / Wang, Zhangyu (Autor:in)

    Kongress:

    Automotive Technical Papers ; 2022



    Erscheinungsdatum :

    2021-03-30




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch






    THREE-DIMENSIONAL OBJECT DETECTION DEVICE, THREE-DIMENSIONAL OBJECT DETECTION METHOD

    HAYAKAWA YASUHISA / FUKATA OSAMU / OIKI DAISUKE et al. | Europäisches Patentamt | 2018

    Freier Zugriff

    Three-dimensional object detection device, three-dimensional object detection method

    HAYAKAWA YASUHISA / FUKATA OSAMU / OIKI DAISUKE et al. | Europäisches Patentamt | 2015

    Freier Zugriff

    THREE-DIMENSIONAL OBJECT DETECTION DEVICE AND THREE-DIMENSIONAL OBJECT DETECTION METHOD

    FUKATA OSAMU / HAYAKAWA YASUHISA | Europäisches Patentamt | 2015

    Freier Zugriff