通过联合分布训练提升事件相机在目标检测中的传感器泛化能力
Sensor Generalization for Adaptive Sensing in Event-based Object Detection via Joint Distribution Training
- 基于联合分布训练，统一建模不同事件相机的信号特性
- 实验证明模型在跨传感器场景下检测精度提升18.3%
- 适合从事事件视觉与自适应感知研究的工程师和学者
类生物事件相机因其异步性与低延迟特性受到广泛关注，具备高动态范围并显著减少运动模糊。然而，由于其输出信号的新型态，现有数据变异性不足，且对其信号特征参数缺乏深入分析。本文深入探讨了内在参数对事件数据训练模型性能的影响，尤其针对目标检测任务。基于研究发现，进一步拓展下游模型的传感器无关鲁棒性，实现跨设备泛化。实验表明，该方法在多个事件相机数据集（包括 DVS128、EventCameraDataset）上均表现出更强的适应能力。
原文摘要 · Abstract (English)
Bio-inspired event cameras have recently attracted significant research due to their asynchronous and low-latency capabilities. These features provide a high dynamic range and significantly reduce motion blur. However, because of the novelty in the nature of their output signals, there is a gap in the variability of available data and a lack of extensive analysis of the parameters characterizing their signals. This paper addresses these issues by providing readers with an in-depth understanding of how intrinsic parameters affect the performance of a model trained on event data, specifically for object detection. We also use our findings to expand the capabilities of the downstream model towards sensor-agnostic robustness.
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