Wearable ultraviolet sensor based on convolutional neural network image processing method

可穿戴计算机 卷积神经网络 计算机科学 光强度 图像处理 图像传感器 紫外线 人工智能 计算机视觉 实时计算 计算机硬件 嵌入式系统 图像(数学) 材料科学 光电子学 光学 物理
作者
Yan Chen,Zimei Cao,Jiejian Zhang,Yuanqing Liu,Duli Yu,Xiaoliang Guo
出处
期刊:Sensors and Actuators A-physical [Elsevier]
卷期号:338: 113402-113402 被引量:13
标识
DOI:10.1016/j.sna.2022.113402
摘要

The wearable sensors based on image processing possess distinct advantages such as being power-free and without complex wire connections, which are of low cost and easy to manufacture. In this paper, a wearable UV sensor made from photochromic material and PDMS was proposed to be employed in real-time UV monitoring and daily solar protection. The convolutional neural network image processing method was introduced and developed for quantifying UV intensity, and it was shown to decrease the impact of ambient light significantly. The limit of detection of the sensor was about 9 μW/cm2 and the recognition rate of the network exceeded 90% under different ambient light conditions. The CNN test was complete within 3 s. Finally, regarding applied scenarios, a UV intensity recognition APP based on a mobile convolutional neural network was designed, which displayed the real-time UV intensity by simple photting.

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