发表机构
National Natural Science Foundation of China（中国国家自然科学基金委员会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对动态场景下事件视觉易受噪声干扰、与生物神经处理融合不足的问题，提出融合事件编码与类蝇视神经网的框架，可高效实现实时运动检测。
AI 中文摘要
快速可靠的运动检测是动态环境下机器视觉与自主系统的核心需求。本研究将新兴的事件传感技术与结构化生物神经计算相结合，构建了视觉运动检测的高效计算范式。所提框架基于最新开发的类蝇神经网络搭建，该网络模拟昆虫视神经叶内的运动处理回路，凭借前向无训练架构仅需少量可解释参数，适配实时嵌入式部署。事件相机通过异步传输亮度变化事件实现低延迟、低功耗、高动态范围的视觉感知，但其性能易受时序噪声、结漏诱发活动等事件噪声干扰，低光照场景下问题尤为突出，且事件视觉表征与生物启发神经处理的有效融合仍有待探索。为解决上述挑战，本研究提出事件驱动计算框架，前端采用时间面编码实现事件表征，后端接入类蝇视神经叶神经网络完成前景运动方向估计，进一步引入自下而上的注意力机制抑制背景运动、提升前景目标显著性。在真实地面车辆数据集上开展测试，与基于帧的基线模型、优化类方法进行对比，实验结果表明该框架有效结合了事件驱动视觉的时序优势与生物启发神经处理的高效性、可解释性。
英文摘要
Fast and reliable motion detection is essential for machine vision and autonomous systems operating in dynamic environments. This work integrates emerging event-based sensing with biologically structured neural computation to establish an efficient computational paradigm for visual motion detection. The proposed framework is built upon a recently developed fly-inspired neural network that emulates motion-processing circuits in the optic lobe. Owing to its feed-forward and training-free architecture, the neural model requires only a small number of interpretable parameters and is well suited for real-time implementation. Event cameras provide low-latency, low-power, and high-dynamic-range visual sensing by asynchronously transmitting brightness-change events. However, their performance can be degraded by event noise, including temporal noise and junction-leakage-induced activity, particularly under low-light conditions. Moreover, effective integration between event-based visual representations and biologically inspired neural processing remains under-explored. To address these challenges, we propose an event-driven computational framework that combines time-surface encoding for front-end event representation with a fly optic-lobe-inspired neural network for foreground motion-direction estimation. A bottom-up attention mechanism is further incorporated to suppress background motion and enhance the saliency of foreground targets. The proposed method is evaluated via real-world datasets of ground-vehicle detection and compared with a baseline frame-based model and an optimization-based approach. Experimental results demonstrate that the framework effectively combines the temporal advantages of event-driven vision with the efficiency and interpretability of bio-inspired neural processing.
Comments6 pages, 5 figures, conference
