Event cameras, with their high dynamic range and temporal resolution, are ideally suited for object detection, especially under scenarios with motion blur and challenging lighting conditions. However, while most existing approaches prioritize optimizing spatiotemporal representations with advanced detection backbones and early aggregation functions, the crucial issue of adaptive event sampling remains largely unaddressed. Spiking Neural Networks (SNNs), which operate on an event-driven paradigm through sparse spike communication, emerge as a natural fit for addressing this challenge. In this study, we discover that the neural dynamics of spiking neurons align closely with the behavior of an ideal temporal event sampler. Motivated by this insight, we propose a novel adaptive sampling module that leverages recurrent convolutional SNNs enhanced with temporal memory, facilitating a fully end-to-end learnable framework for event-based detection. Additionally, we introduce Residual Potential Dropout (RPD) and Spike-Aware Training (SAT) to regulate potential distribution and address performance degradation encountered in spike-based sampling modules. Through rigorous testing on neuromorphic datasets for event-based detection, our approach demonstrably surpasses existing state-of-the-art spike-based methods, achieving superior performance with significantly fewer parameters and time steps. For instance, our method achieves a 4.4\% mAP improvement on the Gen1 dataset, while requiring 38\% fewer parameters and three time steps. Moreover, the applicability and effectiveness of our adaptive sampling methodology extend beyond SNNs, as demonstrated through further validation on conventional non-spiking detection models.

翻译：事件相机凭借其高动态范围和时间分辨率，在目标检测领域具有天然优势，尤其适用于运动模糊和光照条件苛刻的场景。然而，现有方法大多致力于通过先进检测主干网络和早期聚合函数优化时空表示，却对自适应事件采样这一关键问题关注不足。脉冲神经网络（SNN）通过稀疏脉冲通信实现事件驱动范式，天然契合该挑战。本研究发现，脉冲神经元的神经动态行为与理想时间事件采样器高度吻合。受此启发，我们提出一种新颖的自适应采样模块，通过具有时间记忆能力的递归卷积SNN增强，构建面向事件检测的完全端到端可学习框架。此外，我们引入残差电位丢弃（RPD）与脉冲感知训练（SAT）调控电位分布，解决基于脉冲采样模块的性能退化问题。通过神经形态数据集上的严格测试，本方法在事件检测任务中显著超越现有最优脉冲方法，以更少的参数与时序步数实现卓越性能。例如，本方法在Gen1数据集上实现4.4%的平均精度提升，同时减少38%参数并仅需三个时间步。值得注意的是，本自适应采样方法的适用性与有效性超越了SNN框架——其在传统非脉冲检测模型上的进一步验证也证明了这一点。