Object detection in event streams has emerged as a cutting-edge research area, demonstrating superior performance in low-light conditions, scenarios with motion blur, and rapid movements. Current detectors leverage spiking neural networks, Transformers, or convolutional neural networks as their core architectures, each with its own set of limitations including restricted performance, high computational overhead, or limited local receptive fields. This paper introduces a novel MoE (Mixture of Experts) heat conduction-based object detection algorithm that strikingly balances accuracy and computational efficiency. Initially, we employ a stem network for event data embedding, followed by processing through our innovative MoE-HCO blocks. Each block integrates various expert modules to mimic heat conduction within event streams. Subsequently, an IoU-based query selection module is utilized for efficient token extraction, which is then channeled into a detection head for the final object detection process. Furthermore, we are pleased to introduce EvDET200K, a novel benchmark dataset for event-based object detection. Captured with a high-definition Prophesee EVK4-HD event camera, this dataset encompasses 10 distinct categories, 200,000 bounding boxes, and 10,054 samples, each spanning 2 to 5 seconds. We also provide comprehensive results from over 15 state-of-the-art detectors, offering a solid foundation for future research and comparison. The source code of this paper will be released on: https://github.com/Event-AHU/OpenEvDET

翻译：事件流中的目标检测已成为前沿研究领域，在低光照条件、运动模糊场景及快速运动情况下展现出卓越性能。当前检测器以脉冲神经网络、Transformer或卷积神经网络为核心架构，各自存在性能受限、计算开销大或局部感受野有限等局限性。本文提出一种新颖的基于MoE（专家混合）热传导的目标检测算法，在精度与计算效率间实现显著平衡。首先采用主干网络进行事件数据嵌入，随后通过创新的MoE-HCO模块进行处理。每个模块集成多种专家模块以模拟事件流内的热传导过程。继而采用基于IoU的查询选择模块进行高效令牌提取，最终输入检测头完成目标检测流程。此外，我们荣幸推出事件目标检测新型基准数据集EvDET200K。该数据集采用高清Prophesee EVK4-HD事件相机采集，涵盖10个不同类别、200,000个边界框及10,054个样本，每个样本持续2至5秒。我们还提供了超过15种前沿检测器的完整评估结果，为未来研究与比较奠定坚实基础。本文源代码将发布于：https://github.com/Event-AHU/OpenEvDET