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Proceedings Paper

Hazardous gas detection for FTIR-based hyperspectral imaging system using DNN and CNN
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Paper Abstract

Recently, a hyperspectral imaging system (HIS) with a Fourier Transform InfraRed (FTIR) spectrometer has been widely used due to its strengths in detecting gaseous fumes. Even though numerous algorithms for detecting gaseous fumes have already been studied, it is still difficult to detect target gases properly because of atmospheric interference substances and unclear characteristics of low concentration gases. In this paper, we propose detection algorithms for classifying hazardous gases using a deep neural network (DNN) and a convolutional neural network (CNN). In both the DNN and CNN, spectral signal preprocessing, e.g., offset, noise, and baseline removal, are carried out. In the DNN algorithm, the preprocessed spectral signals are used as feature maps of the DNN with five layers, and it is trained by a stochastic gradient descent (SGD) algorithm (50 batch size) and dropout regularization (0.7 ratio). In the CNN algorithm, preprocessed spectral signals are trained with 1 × 3 convolution layers and 1 × 2 max-pooling layers. As a result, the proposed algorithms improve the classification accuracy rate by 1.5% over the existing support vector machine (SVM) algorithm for detecting and classifying hazardous gases.

Paper Details

Date Published: 6 October 2017
PDF: 9 pages
Proc. SPIE 10433, Electro-Optical and Infrared Systems: Technology and Applications XIV, 1043317 (6 October 2017); doi: 10.1117/12.2279077
Show Author Affiliations
Yong Chan Kim, KAIST (Korea, Republic of)
Hyeong-Geun Yu, KAIST (Korea, Republic of)
Jae-Hoon Lee, KAIST (Korea, Republic of)
Dong-Jo Park, KAIST (Korea, Republic of)
Hyun-Woo Nam, Agency for Defense Development (Korea, Republic of)


Published in SPIE Proceedings Vol. 10433:
Electro-Optical and Infrared Systems: Technology and Applications XIV
David A. Huckridge; Reinhard Ebert; Helge Bürsing, Editor(s)

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