Artificial neural networks (ANN) have shown remarkable performance in intelligent transportation systems (ITS), especially for the traffic object detection. However, as the ITS is applied to a wider range of traffic scenarios, the increasing demand for the trade-off between detection performance and power resources has become inevitable. A biologically interpretable spike-driven traffic object detector for challenging scenarios is proposed in this paper, named SpikeTOD, achieving the trade-off between the accuracy and power consumption. Firstly, the spike neural network (SNN) is employed to realize energy-efficient object detection in traffic scenarios. And a local modulation-based integrate-and-fire (IF) neuron is designed, which provides an efficient way to convert the traffic detection model from ANN to SNN. Secondly, a biology-inspired detail-guided context-aware network (DCNet) is proposed to improve the detection performance. The integration of detail coherence and global priors is leveraged to selectively emphasize object features and improve the detection capabilities within challenging conditions. As far as we know, this is the first application of SNN in traffic object detection tasks. SpikeTOD achieved a mAP@50 of 46.11% on the BDD100K dataset with a power consumption of 4.73E-03J, demonstrating a more efficient trade-off in detection accuracy and power consumption. Notably, SpikeTOD maintained an average missed detection rate of 44.56%, further contributing to its overall efficacy in traffic object detection. Further, we conducted on road test by deploying SpikeTOD on Jetson Xavier NX and Loihi to demonstrate that model achieves a better balance between accuracy and power consumption.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    SpikeTOD: A Biologically Interpretable Spike-Driven Object Detection in Challenging Traffic Scenarios


    Beteiligte:
    Wang, Junfan (Autor:in) / Chen, Yi (Autor:in) / Ji, Xiaoyue (Autor:in) / Dong, Zhekang (Autor:in) / Gao, Mingyu (Autor:in) / He, Zhiwei (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.12.2024


    Format / Umfang :

    13669494 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Robust Lane Detection and Tracking in Challenging Scenarios

    Kim, Z. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2008


    MULTI-VEHICLE INTERACTION SCENARIOS GENERATION WITH INTERPRETABLE TRAFFIC PRIMITIVES AND GAUSSIAN PROCESS REGRESSION

    Zhang, Weiyang / Wang, Wenshuo / Zhu, Jiacheng et al. | British Library Conference Proceedings | 2020