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2D时间延伸的无otropic突触实现在传感器中的强度-跨度视觉特征融合

Decai Ouyang¹, Mengqi Wang¹, Na Zhang¹

¹State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology (HUST), Wuhan, 430074, China.

Advanced materials (Deerfield Beach, Fla.)

|June 6, 2025

概括

一个新的2D时间延伸无otropic Synapse (2D TSAS) 能够实现单特征融合,用于高动态范围成像. 这项技术通过克服延迟和隐私问题来推进实时智能视觉系统.

科学领域:

材料科学材料科学材料科学
神经形态工程的神经形态工程
计算机视觉计算机视觉

背景情况:

传统的成像在高动态范围 (HDR) 环境中扎,原因是多处理的延迟和隐私问题.
实时和边缘级智能视觉需要高效,低延迟的解决方案来处理极端亮度变化.

研究的目的:

开发一种传感器内解决方案,用于在HDR视觉环境中使用单个图像进行特征融合.
在极端亮度域中实现单拍视觉学习,用于神经形态预处理.

主要方法:

使用NbOI2材料属性的2D时间延伸无otropic突触 (2D TSAS) 的开发.
整合在平面上的异构性,用于偏振解析的光学响应和时间延伸的光响应,用于多通道的过渡放松.
构建一个神经形态预处理策略,用于一次性视觉学习.

主要成果:

2D TSAS 能够直接编码和时间整合空间偏振和亮度特征.
在神经形态预处理中实现加速模型融合,训练损失最小.
在NWPU-RESISC45上展示了≈95.41%的高识别精度,在MNIST上显示了≈95.39%的精度.

结论:

2D TSAS为复杂环境中的对比适应智能视觉提供了一个紧而高效的解决方案.
这种在传感器中的方法克服了传统HDR成像和基于云的处理的局限性.
在具有挑战性的照明条件下为先进的实时智能视觉应用铺平了道路.

关键词:

两维材料是二维材料.人工突触是一种人造突触.神经形态设备的神经形态设备视觉特征融合视觉特征融合

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