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对于CMOS图像传感器的压缩高效特征提取方法

Keiichiro Kuroda1, Yu Osuka1, Ryoya Iegaki1

  • 1Graduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu-shi 525-8577, Japan.

Sensors (Basel, Switzerland)
|February 13, 2026
PubMed
概括

这项研究介绍了一种用于物联网 (IoT) 提取二进制图像特征的新方法,将数据大小大幅减少99%,同时保持识别精度. 这使得边缘设备的高效,低功耗的图像识别系统成为可能.

关键词:
这是一个CMOS图像传感器.数据减少数据的减少.功能提取 特性提取对象识别对象识别器运行长度编码的编码

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科学领域:

  • 计算机视觉 计算机视觉
  • 图像处理 图像处理
  • 嵌入式系统 嵌入式系统

背景情况:

  • 物联网 (IoT) 时代的电力限制需要高效的数据处理.
  • 传统的图像传感器产生大量的数据,挑战低功耗应用.
  • 现有的方法通常涉及数据大小和识别精度之间的权衡.

研究的目的:

  • 为CMOS图像传感器开发一个压缩效率高的特征提取方法.
  • 为了实现物联网时代的低功耗图像识别系统.
  • 为了减少数据传输量而不会影响图像识别准确度.

主要方法:

  • 为CMOS图像传感器提出了一种新的特征提取方法.
  • 提取的六通道二进制特征数据 (亮度和水平边缘信号).
  • 使用运行长度编码 (RLE) 来进行数据压缩.

主要成果:

  • 在COCO数据集上使用边缘GPU上的YOLOv7实现了60.7%的APL50对象识别精度,数据大小减少了99.2%.
  • 在使用MobileNetV3的Visual Wake Words (VWW) 数据集上实现了89.4%的图像分类准确性,数据大小减少了99.0%.
  • 与传统方法相比,通过显著减少数据大小,同时保持高准确度,表现出优越的性能.

结论:

  • 拟议的方法有效地减少了在电力有限的物联网环境中用于图像识别的数据大小.
  • 这种方法克服了传统的准确性数据大小权衡.
  • 能够实现高效,低功耗的图像识别系统的实际实现.