室内场景识别机制基于方向驱动的卷积神经网络.
Andrea Daou1,2, Jean-Baptiste Pothin2, Paul Honeine1
1Univ Rouen Normandie, INSA Rouen Normandie, Université Le Havre Normandie, Normandie Univ, LITIS UR 4108, F-76000 Rouen, France.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
这项研究介绍了一种使用深度学习和智能手机传感器的新型室内定位系统. 它通过将视觉数据与磁性定位相结合,提高了室级准确度,改进了传统方法.
科学领域:
- 计算机视觉 计算机视觉
- 室内定位局部化 室内定位局部化
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 室内基于位置的服务对于导航和监控至关重要.
- 基于视觉的场景识别由于复杂的室内环境而面临挑战.
- 现有的系统在布局,对象和视角的变化方面扎.
研究的目的:
- 利用深度学习和智能手机传感器开发一个室内定位系统.
- 通过将视觉信息与磁性定位相结合来提高准确性.
- 通过混合移动计算卸载策略来解决计算局限性.
主要方法:
- 一个方向驱动的卷积神经网络 (CNN) 架构,具有多个CNN用于不同的方向.
- 权重融合策略,将各种CNN模型的输出结合起来.
- 混合计算方法将CNN实现分成智能手机和服务器.
主要成果:
- 与传统的CNN相比,拟议的系统实现了更好的房间级定位精度.
- 权重融合策略提高了整体系统性能.
- 在混合移动计算卸载中的模型分区证明是有益的.
结论:
- 开发的系统为室内定位提供了强大而有效的解决方案.
- 视觉和磁性数据的整合大大提高了准确性.
- 混合移动计算卸载是资源有限的设备的可行策略.
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