神经形态目标检测：深入研究与未来方向
Neuromorphic Object Detection: An In-Depth Study and Future Directions
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中文总结 AI 辅助
针对传统相机在特定条件下目标检测的挑战，本文对神经形态目标检测算法进行全面调查和基准测试，从多视角探索现有方法，评估多种模型并分析结果，讨论未解决问题，为该领域研究提供资源与方向。
中文摘要 AI 辅助
传统基于帧的相机在高速运动模糊或低光环境下检测物体面临重大挑战。神经形态相机提供具有高时间分辨率和宽动态范围的异步视觉流，为挑战性条件下的目标检测提供了有前景的解决方案。尽管神经形态目标检测有众多模型发展和应用出现，但仍缺乏深入理解和标准化基准。本文对现有神经形态目标检测算法进行全面调查和基准测试。首先进行问题描述、回顾可用数据集并重新审视评估指标；然后从多个角度探索现有方法；接着评估多种代表性模型并分析比较结果；最后讨论未解决问题并提出未来研究方向，希望为研究者提供有价值资源并指导未来进展。
英文摘要
Conventional frame-based cameras face significant challenges in detecting objects under high-speed motion blur or in low-light environments. Neuromorphic cameras provide asynchronous visual streams with high temporal resolution and a wide dynamic range, offering a promising solution for object detection under challenging conditions. Despite the development of numerous models and the emergence of various applications in neuromorphic object detection, there is still a lack of deep understanding and standardized benchmarks to assess progress and address key challenges. In this paper, we provide a comprehensive survey and benchmark of existing neuromorphic object detection algorithms. Specifically, we first present a problem description, review the available datasets, and revisit the evaluation metrics. We then explore existing neuromorphic object detection approaches from various perspectives, including event representation, temporal modeling, multimodal fusion, asynchronous processing, low-latency processing, and energy-efficient computing. Furthermore, we evaluate a wide range of representative neuromorphic object detection models and offer detailed analyses of the comparative results. Finally, we discuss unresolved issues in neuromorphic object detection and propose potential future research directions. We hope this survey and benchmark will be a valuable resource for researchers and provide guidance for future advancements in neuromorphic object detection.
发表机构
- Peking University（北京大学）
- Peng Cheng Laboratory（鹏城实验室）
- Harbin Institute of Technology（哈尔滨工业大学）
- Chinese Academy of Sciences（中国科学院）
- Italian Institute of Technology（意大利理工学院）
- Carnegie Mellon University（卡内基梅隆大学）
- University of Pittsburgh（匹兹堡大学）
- Sorbonne Université（索邦大学）
- INSERM（法国国家健康与医学研究院）
- CNRS（法国国家科学研究中心）
- Institut de la Vision（视觉研究所）
机构由 AI 辅助整理，请以论文原文为准。
