Vehicle and pedestrian detection is one of the critical tasks in autonomous driving. Since heterogeneous techniques have been proposed, the selection of a detection system with an appropriate balance among detection accuracy, speed and memory consumption for a specific task has become very challenging. To deal with this issue and to provide guidance for model selection, this paper analyzes several mainstream object detection architectures, including Faster R-CNN, R-FCN, and SSD, along with several typical feature extractors, such as ResNet50, ResNet101, MobileNet_V1, MobileNet_V2, Inception_V2 and Inception_ResNet_V2. By conducting extensive experiments using the KITTI benchmark, which is a commonly used street dataset, we demonstrate that Faster R-CNN ResNet50 obtains the best average precision (AP) (58%) for vehicle and pedestrian detection, with a speed of 8.6 FPS. Faster R-CNN Inception_V2 performs best for detecting cars and detecting pedestrians respectively (74.5% and 47.3%). ResNet101 consumes the highest memory (9907 MB) and has the largest number of parameters (64.42 millions), and Inception_ResNet_V2 is the slowest model (3.05 FPS). SSD MobileNet_V2 is the fastest model (70 FPS), and SSD MobileNet_V1 is the lightest model in terms of memory usage (875 MB), both of which are suitable for applications on mobile and embedded devices.


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    Titel :

    Deep Neural Network Based Vehicle and Pedestrian Detection for Autonomous Driving: A Survey


    Beteiligte:
    Chen, Long (Autor:in) / Lin, Shaobo (Autor:in) / Lu, Xiankai (Autor:in) / Cao, Dongpu (Autor:in) / Wu, Hangbin (Autor:in) / Guo, Chi (Autor:in) / Liu, Chun (Autor:in) / Wang, Fei-Yue (Autor:in)


    Erscheinungsdatum :

    2021-06-01


    Format / Umfang :

    8526239 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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