Highlights A point cloud reconstruction method is adopted to obtain a dense 3D truck point cloud. PointNet is utilized to classify trucks’ point clouds in detail. Ensemble methods are used to enhance the performance of the classification model. The performance of this truck classification model doesn’t vary by traffic conditions.

    Abstract Understanding the spatiotemporal distribution of commercial vehicles is essential for facilitating strategic pavement design, freight planning, and policy making. Hence, transportation agencies have been increasingly interested in collecting truck body configuration data due to its strong association with industries and freight commodities, to better understand their distinct operational characteristics and impacts on infrastructure and the environment. The rapid advancement of Light Detection and Ranging (LiDAR) technology has facilitated the development of non-intrusive detection solutions that are able to accurately classify truck body types in detail. This paper proposes a new truck classification method using a LiDAR sensor oriented to provide a wide field-of-view of roadways. In order to enrich the sparse point cloud obtained from the sensor, point clouds originating from the same truck across consecutive frames were grouped and combined using a two-stage vehicle reconstruction framework to generate a dense three-dimensional (3D) point cloud representation of each truck. Subsequently, PointNet – a deep representation learning algorithm – was adopted to train the classification model from reconstructed point clouds. The model utilizes low-level features extracted from the 3D point clouds and detects key features associated with each truck class. Finally, model ensemble techniques were explored to reduce the generalization error by averaging the results of seven PointNet models and further enhancing the overall model performance. The optimal number of models in the ensemble was determined through a comprehensive sensitivity analysis with the consideration of the average correct classification rate (CCR), the variability of the prediction results, and the computation efficiency. The developed model is capable of distinguishing passenger vehicles and 29 different truck body configurations with an average CCR of 83 percent. The average correct classification rate of the developed method on the test dataset was 90 percent for trucks pulling a large trailer(s).


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Truck body type classification using a deep representation learning ensemble on 3D point sets


    Beteiligte:
    Li, Yiqiao (Autor:in) / Allu, Koti Reddy (Autor:in) / Sun, Zhe (Autor:in) / Tok, Andre Y.C. (Autor:in) / Feng, Guoliang (Autor:in) / Ritchie, Stephen G. (Autor:in)


    Erscheinungsdatum :

    2021-10-31




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Classification of truck body types using a deep transfer learning approach

    Nezafat, Reza Vatani / Salahshour, Behrouz / Cetin, Mecit | IEEE | 2018


    Truck Body-Type Classification using Single-Beam Lidar Sensors

    Asborno, Magdalena I. / Burris, Collin G. / Hernandez, Sarah | Transportation Research Record | 2019


    Ensemble Deep Learning for Sustainable Multimodal UAV Classification

    McCoy, James / Rawal, Atul / Rawat, Danda B. et al. | IEEE | 2023


    Truck and Trailer Classification With Deep Learning Based Geometric Features

    He, Pan / Wu, Aotian / Huang, Xiaohui et al. | IEEE | 2021


    WING BODY TYPE TRUCK

    OTA TORU | Europäisches Patentamt | 2020

    Freier Zugriff