AI 中文总结
该研究提出序列感知脉冲目标检测器Sequence-SOD，通过保留膜电位处理扩展事件序列，在Gen1数据集上提升目标检测mAP，为事件相机SNN检测提供新方向。
AI 中文摘要
事件相机遵循视网膜启发的传感原理，以高时间分辨率和宽动态范围异步报告局部强度变化。脉冲神经网络（SNN）通过类脑动力学补充这些稀疏事件流，利用稀疏脉冲和泄漏膜电位随时间整合信息。然而，许多SNN目标检测器处理孤立的事件区间并赋予单一标签，且在每次预测后重置网络状态，从而未能充分利用连续事件流中的时间信息。我们提出Sequence-SOD，这是一种序列感知SNN目标检测器，可处理包含多个时间点标签的扩展事件序列。事件被累积为短区间，离散化为时间步长，并依次输入到SSD风格的脉冲DenseNet中，同时保留序列内各区间的膜电位，使检测由演化的神经状态驱动，而非独立重置的输入窗口。在Gen1汽车检测数据集上，序列感知训练使mAP从单区间训练的23.38提升至无数据增强时的25.30，经事件数据增强后进一步提升至26.88。该模型达到40 Hz的理论预测频率。在扩展事件序列上训练和评估SNN目标检测器，提升了其利用时间线索的能力，同时保留了稀疏脉冲计算的能效优势。研究结果表明，序列感知训练是基于事件的SNN检测中，与架构改进互补的研究方向。
英文摘要
Event cameras follow a retina-inspired sensing principle, reporting local intensity changes asynchronously with hightemporal resolution and a wide dynamic range. Spiking Neural Networks (SNNs) complement these sparse event streams through brain-inspired dynamics, using sparse spikes and leaky membrane potentials to integrate information over time. However, many SNN object detectors process isolated event intervals with a single label and reset the network state after each prediction, thereby underusing temporal information in continuous event streams. We introduce Sequence-SOD, a sequence-aware SNN object detector that processes extended event sequences containing labels at multiple time points. Events are accumulated into short intervals, discretized into temporal steps, and fed sequentially to an SSD-style Spiking DenseNet while preserving membrane potentials across intervals within a sequence, so that detection is driven by an evolving neural state instead of independently reset input windows. On the Gen1 Automotive Detection Dataset, sequence-aware training improves mAP from 23.38 for single-interval training to 25.30 without augmentation and to 26.88 withevent-data augmentation. The model achieves a theoretical prediction frequency of 40 Hz. Training and evaluating SNN object detectors on extended event sequences improves their ability to exploit temporal cues while preserving the energy-efficiency benefits of sparse spiking computation. The results highlight sequence-aware training as a complementary direction to architectural improvements for event-based SNN detection.
Journal refCognitive Computation, vol. 18, Article 91 (2026)
DOI：10.1007/s12559-026-10637-z
