走向持续性：学习拓扑约束用于基于事件的小目标检测
Towards Persistence: Learning Topological Constraints for Event-based Small Object Detection
针对事件相机数据中的小目标检测，提出拓扑学习模块和空间一致性模块来约束时空轨迹的连续性，并设计拓扑感知损失函数，为事件流上的目标检测与轨迹完整性提供了可复用的建模思路。
原文依据 we propose a topology-constrained sparse convolutional network (SpTopoNet), which models the topological structure of moving object trajectories in event point clouds.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论小目标检测（SOD）在反无人机等应用中起着至关重要的作用，然而传统的基于图像的方法由于帧率有限，在高速场景中难以奏效。事件相机通过捕捉具有微秒级时间分辨率的时空事件流，提供了一种有前景的替代方案。为解决事件数据中小目标的固有稀疏性问题，现有方法通常将检测任务建模为时空点云上的语义分割，以利用长期上下文信息。然而，这些方法往往未能施加有效的时空一致性约束，导致目标轨迹碎片化。为缓解这些问题，我们提出了一种拓扑约束稀疏卷积网络（SpTopoNet），该网络对事件点云中运动目标轨迹的拓扑结构进行建模。我们的网络包含两个关键组件：一个拓扑学习模块（TLM），用于判别局部结构以将真实目标与噪声分离；以及一个空间一致性模块（SCM），用于捕捉长程时空依赖以增强轨迹连续性。此外，我们引入了一种事件拓扑感知损失函数，利用拓扑相关性引导网络保持目标事件模式的结构完整性。在基准数据集上的实验表明，我们的方法在检测性能和轨迹完整性方面均具有优越性。
英文原文摘要 1476 字符
Small object detection (SOD) plays a vital role in applications such as anti-UAV tasks, yet conventional image-based methods struggle in high-speed scenarios due to the limited frame rate. Event cameras offer a promising alternative by capturing spatiotemporal event streams with microsecond-level temporal resolution. To address the inherent sparsity of small objects in event data, existing methods typically formulate the detection task as semantic segmentation on spatiotemporal point clouds to leverage long-term contextual information. However, these methods often fail to enforce effective spatiotemporal consistency constraints, resulting in fragmented object trajectories. To mitigate these problems, we propose a topology-constrained sparse convolutional network (SpTopoNet), which models the topological structure of moving object trajectories in event point clouds. Our network comprises two key components: a Topology Learning Module (TLM) that discriminates local structures to separate genuine targets from noise, and a Spatial Consistency Module (SCM) that captures long-range spatiotemporal dependencies to enhance trajectory continuity. Additionally, we introduce an event topology-aware loss function that leverages topological correlations to guide the network to maintain structural integrity of target event patterns.Experiments on the benchmark dataset demonstrate the superiority of our method in both detection performance and trajectory completeness.