Transfer-learned SpikeYOLO achieves mAP 0.937/0.771 and HOTA 0.701/0.445 on KITTI and BDD100K for two-class automotive detection and tracking, competitive with conventional deep networks.
STDP-based Unsupervised Feature Learning using Convolution- over-time in Spiking Neural Networks for Energy- Efficient Neuromorphic Computing,
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Efficient Perception in Automotive Detection and Tracking Using Neuromorphic Computing
Transfer-learned SpikeYOLO achieves mAP 0.937/0.771 and HOTA 0.701/0.445 on KITTI and BDD100K for two-class automotive detection and tracking, competitive with conventional deep networks.