Surrounding vehicle detection is one of the most important modules for a vision-based driver assistance system (VB-DAS) or an autonomous vehicle. In this paper, we put forward a wireless panoramic camera system for real-time and seamless imaging of the 360-degree driving scene. Using an embedded FPGA design, the proposed panoramic camera system can perform fast image stitching and produce panoramic videos in real-time, which greatly relives the computation and storage burden of a traditional multi-camera-based panoramic system. For surrounding vehicle detection, we present a novel deep convolutional neural network - EZ-Net, which perceives the potential vehicles by using 13 convolutional layers and locates the vehicles by a local non-maximum suppression process. Experimental results demonstrate that, the proposed EZ-Net performs vehicle detection on the panoramic video at a speed of 140 fps while holding a competing accuracy with the state-of-the-art detectors.


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    Titel :

    Surrounding Vehicle Detection Using an FPGA Panoramic Camera and Deep CNNs


    Beteiligte:
    Chen, Long (Autor:in) / Zou, Qin (Autor:in) / Pan, Ziyu (Autor:in) / Lai, Danyu (Autor:in) / Zhu, Liwei (Autor:in) / Hou, Zhoufan (Autor:in) / Wang, Jun (Autor:in) / Cao, Dongpu (Autor:in)


    Erscheinungsdatum :

    01.12.2020


    Format / Umfang :

    3202888 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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