综述脉冲神经网络在目标检测中的数据集、架构与训练方法，助力高效视觉计算。
Event-based Spiking Neural Networks for Object Detection: A Review of Datasets, Architectures, Learning Rules, and Implementation
- 系统梳理151篇论文，归纳脉冲网络在目标检测中的主流架构与学习策略。
- 验证全连接、卷积与循环结构在事件驱动任务中的有效性，对比不同学习方法性能。
- 提供开源代码与仿真资源，适合关注能效计算与类脑硬件的开发者参考。
脉冲神经网络（SNNs）是一种生物启发的范式，为计算机视觉（CV）应用提供了比传统人工神经网络（ANNs）更节能的替代方案。本文系统综述了基于SNN的视觉目标检测任务中所用的数据集、网络架构、学习方法、实现技术及评估方法。基于对151篇期刊和会议论文的分析，本综述总结了：1）全连接、卷积与循环架构的有效性；2）直接无监督、直接有监督及间接学习方法的性能表现；3）在类脑硬件实现中能量消耗、延迟与内存之间的权衡。同时提供一个开源仓库，包含详细的Python代码示例以及构建SNN模型、事件数据处理与仿真的资源。文中还指出了SNN训练、硬件集成的关键挑战及未来发展方向。
原文摘要 · Abstract (English)
Spiking Neural Networks (SNNs) represent a biologically inspired paradigm offering an energy-efficient alternative to conventional artificial neural networks (ANNs) for Computer Vision (CV) applications. This paper presents a systematic review of datasets, architectures, learning methods, implementation techniques, and evaluation methodologies used in CV-based object detection tasks using SNNs. Based on an analysis of 151 journal and conference articles, the review codifies: 1) the effectiveness of fully connected, convolutional, and recurrent architectures; 2) the performance of direct unsupervised, direct supervised, and indirect learning methods; and 3) the trade-offs in energy consumption, latency, and memory in neuromorphic hardware implementations. An open-source repository along with detailed examples of Python code and resources for building SNN models, event-based data processing, and SNN simulations are provided. Key challenges in SNN training, hardware integration, and future directions for CV applications are also identified.
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