Open AccessHCHODetector: Formaldehyde concentration detection based on deep learning
Author(s) -
Zhi-Hao Cao,
Mingfeng Shao,
Aijun Shi,
Hongchun Qu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1848/1/012047
Subject(s) - formaldehyde , deep learning , artificial intelligence , human health , detector , computer science , hsl and hsv , machine learning , pattern recognition (psychology) , computer vision , chemistry , biology , virology , telecommunications , medicine , biochemistry , environmental health , virus
Currently, deep learning technology is developing rapidly. Deep learning is mainly used in the fields of vision and hearing for human beings, but less in the field of olfactory. Formaldehyde is a common gas harmful to human health. However, the traditional methods of Formaldehyde concentration detection are inefficient in some cases. As for this problem, this paper proposes a novel formaldehyde detector namely HCHODetector. Specifically, this detector is based on deep learning and HSV colour space augmentation. Moreover, we propose a novel Mask-guided module and a novel pre-training network to enhance the colour discrimination ability of HCHODetector. As a consequence, the experimental results show that the detection error is within 0.08 mg/m 3 in the actual environment, which provides a new idea for Formaldehyde concentration detection.