计算机视觉和边缘启用实时辅助框架,用于LPWAN紧急信号的视障人士
Ghadah Naif Alwakid1,2, Mamoona Humayun3, Zulfiqar Ahmad4
1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
这项研究引入了一种先进的视力障碍者辅助框架,通过实时物体检测和双通讯通道增强移动性,以实现可靠的导航和紧急警报.
科学领域:
- 辅助技术 辅助技术 辅助技术
- 计算机视觉 计算机视觉
- 边缘计算 边缘计算
背景情况:
- 现有的视力障碍者辅助技术往往缺乏实时,在动态环境中的通用功能.
- 当前的系统在各种室内/室外环境中面临范围,延迟或可靠性方面的权衡.
- 需要综合解决方案,结合物体检测,通信和紧急信号.
研究的目的:
- 为视力障碍者提出一个全面的辅助框架.
- 整合计算机视觉,边缘计算和双通道通信架构.
- 通过实时协助,增强独立的流动性和生活质量.
主要方法:
- 使用YOLOv5深度学习模型进行高性能物体检测 (精度为0.988,回调为0.969,mAP为0.985).
- 实现边缘计算以实现快速,本地处理和决策,卸载中央服务器.
- 设计了一个双通道通信系统:Wi-Fi主链接和LoRaWAN (低功率广域网) 用于紧急警报.
- 整合了一个基于物联网的恐慌按,用于位置标记的紧急传输.
主要成果:
- 在各种现实条件下证明了低延迟和可靠的系统运行.
- 在检测障碍物,路径和辅助工具方面取得了高精度.
- 该系统在动态的室内和室外环境中被证明是有效的.
- 验证了拟议架构的成本效益和可扩展性.
结论:
- 开发的框架显著提高了对视力受损者实时援助.
- 整合YOLOv5,边缘计算和双通讯提供了一个强大的解决方案.
- 该系统具有很大的潜力,可以提高视力受损用户的独立移动性和安全性.
- 该架构适合在需要立即支持的复杂环境中部署.
关键词:
在LPWAN中使用.辅助技术是指辅助技术的使用.计算机视觉 计算机视觉深度学习是一种深度学习.边缘计算是一种边缘计算.医疗保健 医疗保健 医疗保健 医疗保健障碍物检测 障碍物检测 障碍物检测 障碍物检测实时监控实时监控无线通信是一种无线通信.更多相关视频
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