Wearable ultraviolet sensor based on convolutional neural network image processing method

被引：16

作者：

Chen, Yan ^{[1
,4
]}

Cao, Zimei ^{[3
]}

Zhang, Jiejian ^{[5
]}

Liu, Yuanqing ^{[6
]}

Yu, Duli ^{[3
]}

Guo, Xiaoliang ^{[2
,3
]}

机构：

[1] Univ Sci & Technol Beijing, Beijing Engn Res Ctr Ind Spectrum Imaging, Sch Automat & Elect Engn, Beijing 100083, Peoples R China

[2] Beijing Inst Technol, Sch Mechatron Engn, Beijing 100081, Peoples R China

[3] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China

[4] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China

[5] Harbin Inst Technol, Sch Mechatron Engn, Harbin 150001, Peoples R China

[6] City Univ Hong Kong, Hong Kong, Peoples R China

来源：

SENSORS AND ACTUATORS A-PHYSICAL | 2022年 / 338卷

基金：

北京市自然科学基金; 中国国家自然科学基金;

关键词：

UV sensors; Image processing; Convolutional neural network; Photochromic material;

D O I：

10.1016/j.sna.2022.113402

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

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 mu W/cm(2) 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.

引用

页数：8

共 38 条

[1]

Anguelov D, 2015, ABS14094842 CORR

[2] Developing high-sensitivity UV sensors based on ZnO nanorods grown on TiO2 seed layer films using solution immersion method [J].

Asib, N. A. M. ;

Husairi, F. S. ;

Eswar, K. A. ;

Afaah, A. N. ;

Mamat, M. H. ;

Rusop, M. ;

Khusaimi, Z. .

SENSORS AND ACTUATORS A-PHYSICAL, 2020, 302

[3] DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [J].

Chen, Liang-Chieh ;

Papandreou, George ;

Kokkinos, Iasonas ;

Murphy, Kevin ;

Yuille, Alan L. .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (04) :834-848

[4] Temperature dependence of the pyro-phototronic effect in self-powered p-Si/n-ZnO nanowires heterojuncted ultraviolet sensors [J].

Dong, Jianqi ;

Wang, Zhengjun ;

Wang, Xingfu ;

Wang, Zhong Lin .

NANO TODAY, 2019, 29

[5] CNN-Based Methods for Object Recognition With High-Resolution Tactile Sensors [J].

Gandarias, Juan M. ;

Garcia-Cerezo, Alfonso J. ;

Gomez-de-Gabriel, Jesus M. .

IEEE SENSORS JOURNAL, 2019, 19 (16) :6872-6882

[6] Fast R-CNN [J].

Girshick, Ross .

2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), 2015, :1440-1448

[7] Rich feature hierarchies for accurate object detection and semantic segmentation [J].

Girshick, Ross ;

Donahue, Jeff ;

Darrell, Trevor ;

Malik, Jitendra .

2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2014, :580-587

[8]

He K., 2017, P IEEE INT C COMPUTE, P2980, DOI [DOI 10.1109/ICCV.2017.322, 10.1109/ICCV.2017.322]

[9] Deep Residual Learning for Image Recognition [J].

He, Kaiming ;

Zhang, Xiangyu ;

Ren, Shaoqing ;

Sun, Jian .

2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778

[10] NEURAL NETWORKS AND PHYSICAL SYSTEMS WITH EMERGENT COLLECTIVE COMPUTATIONAL ABILITIES [J].

HOPFIELD, JJ .

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES, 1982, 79 (08) :2554-2558

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