反干扰衍射深度神经网络用于多对象识别
Zhiqi Huang1,2, Yufei Liu3, Nan Zhang4,5
1Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, 100081, China.
Light, science & applications
|February 3, 2026
概括
光学神经网络 (ONN) 为对象识别提供光速计算. 这项研究介绍了一种反干扰衍射深度神经网络 (AI D2NN),该网络在复杂的多对象场景中强有力的识别目标.
科学领域:
- 神经形态计算是一种神经形态计算.
- 光学计算是指光学计算
- 人工智能的人工智能是人工智能.
背景情况:
- 光学神经网络 (ONN) 显示出高速,低功耗计算的潜力.
- 目前的ONN正在与多对象识别和干扰作斗争.
- 实际应用受到单个对象分类约束的限制.
研究的目的:
- 开发一种反干扰衍射深度神经网络 (AI D2NN),用于强大的多对象识别.
- 在复杂的场景中克服现有ONN的局限性.
- 为了实现全光学实时目标识别.
主要方法:
- 提出了一个AI D2NN,使用两个传导衍射层.
- 采用深度学习策略来区分目标和干扰.
- 将目标空间信息完全光学地映射到输出灯的功率谱中.
- 分散干扰作为背景噪声.
主要成果:
- 在对40个干扰类别的手写数字进行分类时,获得了87.4%的模拟精度.
- 证明了对类内,类间和动态干扰的强度.
- 框架可扩展到不同的电磁波长.
结论:
- 人工智能D2NN框架有效地识别了具有挑战性的多对象场景中的目标.
- 这一进步对于在目标识别中的实际ONN应用至关重要.
- 为实时,低功耗全光学计算系统铺平了道路.
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