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多人定位基于热阵列传感器与机器学习和生成数据模型.

Stefan Klir1, Julian Lerch1, Simon Benkner1

  • 1Laboratory of Adaptive Lighting Systems and Visual Processing, Technical University of Darmstadt, Hochschulstr. 4a, 64289 Darmstadt, Germany.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
概括
此摘要是机器生成的。

带有机器学习的热传感器可以在低分辨率的热图像中准确地检测和定位人,从而提高智能建筑中的隐私. 一个新的图像生成器提高了精度,即使在具有挑战性的条件下.

关键词:
生成式的IR数据是生成式的IR数据.红外线阵列传感器阵列传感器多人检测检测多人检测人们本地化本地化.热阵列是一个热阵列.

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 传感器技术 传感器技术

背景情况:

  • 热传感器阵列在智能建筑自动化中提供了个人检测和定位的隐私保护方法.
  • 像YOLOv5和DETR这样的机器学习算法在一般物体检测中很突出.

研究的目的:

  • 调整YOLOv5和DETR以在低分辨率 (32x32像素) 热阵列图像中定位人们.
  • 评估这些算法的稳定性和性能,并通过一种新的图像生成技术进行增强.

主要方法:

  • 在热图像中适应YOLOv5和DETR算法用于人定位.
  • 开发一个图像插值 (IIG) 发生器,从稀疏的数据创建合成热.
  • 对各种环境和物体相关干扰进行多次强度测试.

主要成果:

  • 无论是YOLOv5还是DETR,都实现了高平均精度 (mAP) 超过98%.
  • 算法证明了对温暖的空气,阳光辐射,传感器更换和各种运动场景的稳定性.
  • 精确度随着厚厚的衣服以及人数超过训练数据而下降.

结论:

  • 无论是YOLOv5还是DETR都适合使用热传感器进行人体检测和定位.
  • YOLOv5m在实时处理,较小的模型尺寸和略高的精度方面具有优势.
  • 新型图像生成器有效地抵消由于标记数据稀疏而导致的精度损失.