极其早期的图像识别使用基于事件的视觉
Abubakar Abubakar1, AlKhzami AlHarami1, Yin Yang1
1Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了一种使用基于事件的数据的创新图像识别技术,可以在完全捕捉图像之前识别对象. 与传统的基于框架的系统相比,这种方法显著降低了计算负载,存储需求和功耗.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 传感器技术 传感器技术
背景情况:
- 基于的成像器需要高存储,计算和功率.
- 基于事件的成像器通过输出非同步的像素事件来降低功耗和延迟.
研究的目的:
- 提出一种创新的图像识别技术,使用基于事件的数据.
- 在图像完全获取之前实现早期图像识别.
- 为了减少计算开销,存储需求和功耗.
主要方法:
- 开发了一种基于事件数据而不是基于数据的图像识别技术.
- 利用收集的基于事件的数据集 (CeleX 图像) 和五个基于公共事件的数据集进行验证.
- 使用神经网络 (NN) 测试指标来评估早期检测时间.
主要成果:
- 在第一个完美事件之前的平均38.7ms和最后一个事件之前的603.4ms中识别了图像.
- 实现了分别减少了34%和69%的认可所需时间.
- 与等待第一个完美识别的图像相比,处理减少了37% (9460事件之前).
结论:
- 拟议的技术允许使用基于事件的数据进行极端早期的图像识别.
- 这种方法显著降低了计算开销,存储和功耗.
- 一种增强的NN方法进一步优化了识别时间,建立了图像识别的新范式.
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