用脉冲神经网络实现低功耗汽车目标检测与跟踪，性能媲美传统深度学习。
Efficient Perception in Automotive Detection and Tracking Using Neuromorphic Computing
- 采用脉冲神经网络结合迁移学习，提升能效。
- KITTI数据集检测精度达0.937，追踪准确率0.701。
- 适合对能耗敏感的自动驾驶感知系统部署。
深度学习算法因高碳足迹和计算需求，限制了其在边缘设备的部署，并引发长期可持续性担忧。类脑计算与脉冲神经网络（SNNs）为传统冯·诺依曼架构提供了高效替代方案，具备低功耗、大规模并行计算及片上学习能力。自动驾驶系统是这些优势的关键应用场景。本文首次全面评估SNNs在真实世界汽车多目标检测与追踪中的表现。基于SpikeYOLO架构的迁移学习，在KITTI数据集上实现0.937的平均精度（mAP），BDD100K MOT2020上为0.771；追踪方面，KITTI得分为0.701，BDD100K MOT2020为0.445，结果与传统深度学习方法相当。实验证明SNNs可在极低能耗下实现高性能感知，具备在真实自动驾驶系统中应用的可行性。
原文摘要 · Abstract (English)
Deep learning algorithms are notorious for their high carbon footprint and computational demands that limit their deployment on edge devices and raise concerns about their long-term sustainability. Neuromorphic computing and Spiking Neural Networks (SNNs) offer a promising alternative to traditional Von Neumann architectures, providing energy-efficient performance, massively parallel computation, and on-chip learning capabilities. Autonomous machines represent a critical application domain where these advantages are particularly valuable. We present the first comprehensive evaluation of SNNs for real-world automotive multi-object detection and tracking. Using transfer learning with the SpikeYOLO architecture, we achieve mean Average Precision of 0.937 on the KITTI dataset and 0.771 on BDD100K MOT2020 dataset for object detection and a Higher Order Tracking Accuracy score of 0.701 (KITTI) and 0.445 (BDD100K MOT2020) for object tracking--results competitive with conventional deep learning methods. Our results demonstrate that SNNs can deliver high-performance object detection and tracking in an energy efficient manner, establishing their viability for perception in real-world autonomous systems.
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