基于深度学习的高精度检测和分类模型在色差键化光学摄像头通信系统中
Francisca V Vera Vera1, Leonardo Muñoz1, Francisco Pérez1
1Department of Electrical Engineering, Universidad de Concepción, Edmundo Larenas 219, Concepción 4030000, Chile.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
光学摄像头通信 (OCC) 提供了使用设备摄像头的低成本无线替代方案. 一个新的深度学习模型在识别颜色转移键 (CSK) 符号方面实现了98.4%的准确性,证明了物联网应用的强大性能.
科学领域:
- 光学无线通信的无线通信.
- 深度学习应用程序
- 无线网络无线网络.
背景情况:
- 传统的无线电频率网络因越来越多的连接设备而面临压力.
- 光学无线通信 (OWC) 成为一个可行的替代方案.
- 光学摄像头通信 (OCC) 使用现有配备摄像头的设备提供了具有成本效益的OWC解决方案.
研究的目的:
- 提出和验证一种新的深度学习模型,以提高光学摄像头通信 (OCC) 接收器的性能.
- 为了优化OCC系统使用颜色转移键 (CSK) 调制.
- 为了证明基于摄像头的接收器可用于可靠的数据传输的可行性.
主要方法:
- 开发基于深度学习的检测和分类模型.
- 使用8x8LED矩阵发射器和CMOS摄像机接收器进行实验验证.
- 实现颜色转移键化 (CSK) 调制,将数据编码成八种不同的颜色符号.
- 使用基于YOLOv8的框架进行符号识别的捕获图像序列的处理.
主要成果:
- 在符号识别方面,YOLOv8检测和分类框架实现了98.4%的准确性.
- 在各种环境条件下,在30厘米至3米的距离上证明了可靠的通信.
- 该系统在现实环境中表现出强性,最大限度地减少了传输错误.
结论:
- 拟议的深度学习方法显著提高了OCC接收器的性能.
- 特别是在CSK调制和深度学习方面,OCC为物联网和车辆对车辆通信等特定应用提供了有希望的低成本解决方案.
- 未来的研究方向包括自适应调制,编码方案和先进的深度学习架构,以提高数据速率和可扩展性.
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