PMRVT:平行注意力多层感知子反复视觉变压器,用于用事件摄像头进行对象检测
Zishi Song1, Jianming Wang2, Yongxin Su3
1School of Electronics and Information, Tiangong University, Tianjin 300387, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了PMRVT,这是基于事件的对象检测的新框架. 在动态环境中,PMRVT通过平衡效率,空间表达力和时间一致性来提高实时性能和准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 由于运动模糊和延迟,在高速,动态环境中对象检测对传统相机来说具有挑战性.
- 事件摄像头提供了一个具有高动态范围和低延迟的替代方案,但现有的方法具有高的计算成本和有限的时间建模.
- 基于实时事件的视觉应用需要提高检测准确性和效率.
研究的目的:
- 介绍PMRVT (平行注意力多层感知器反复视觉转换器),这是基于事件的对象检测的统一框架.
- 在基于事件的检测中,系统地平衡早期效率,空间表现力和长期时间一致性.
- 为了在动态环境中实现高精度和实时性能.
主要方法:
- 开发了一个混合层次的骨干,以实现高效的处理.
- 实施了一种并行注意特征融合 (PAFF) 机制,采用协调的双路径设计.
- 采用时间整合策略,以提高一致性.
主要成果:
- 在Gen1数据集上,PMRVT实现了48.7%的mAP,在1Mpx数据集上达到48.6%的mAP.
- 推断延迟时间为7.72毫秒 (Gen1) 和19.94毫秒 (1Mpx).
- 与最先进的方法相比,证明了1.5pp的精度提高和8%的延迟减少.
结论:
- 在基于事件的对象检测中,PMRVT提供了准确性和速度之间的良好平衡.
- 该框架为基于实时事件的视觉应用提供了可靠的解决方案.
- PMRVT解决了计算成本和时间建模现有方法的局限性.
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