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机器视觉驱动的微阵列被动温度传感器灵感来自昆虫复合眼睛,用于广泛和高精度的表面映射.

Potao Sun¹, Wenqing Yang¹, Wenxia Sima¹

¹State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China.

ACS applied materials & interfaces

|July 24, 2025

概括

这项研究引入了一种新的微阵列被动温度传感器 (MAPTS),用于准确的实时监测. 机器视觉系统利用深度学习在工业应用中精确,非接触式温度预测.

关键词:

深度学习是一种深度学习.机器视觉机器视觉机器视觉微阵列传感器是一个微阵列传感器.多道协作感知多道协作感知通过被动的温度传感.热色材料是一种热色材料.

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科学领域:

材料科学材料科学材料科学
传感器技术传感器技术
人工智能的人工智能

背景情况:

准确的,被动的温度监测对于工业和科学领域至关重要.
传统传感器面临诸如功率要求,复杂仪器和热场扰动等局限性.
现有的热色材料对被动传感具有有限的灵敏度和主观解释.

研究的目的:

开发一款支持机器视觉的微阵列被动温度传感器 (MAPTS),用于高精度的非接触式温度监测.
克服传统温度传感器和主观热色解释的局限性.
通过一种具有成本效益和可靠的方法,在各种应用中实现智能温度监控.

主要方法:

在柔性基板上制造有机热色材料阵列,使用软光刻.
开发基于深度学习的ResNet-34架构,用于颜色与温度的映射.
实现用于光学图像采集和分析的机器视觉系统.

主要成果:

MAPTS在0-70°C的广泛范围内显示出动态热反应.
实现了快速温度预测,响应时间为50毫秒.
在7×7阵列配置中观察到高预测准确度 (平均绝对误差≤0.3°C) 和优异的推断性能 (R2=0.9996).

结论:

MAPTS为智能温度监控提供了具有成本效益,高度准确和可靠的解决方案.
这种新型传感器克服了传统方法的局限性,允许精确的非接触测量.
该系统的性能表明,对于需要被动温度传感的各种工业和科学应用,其潜力很大.