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http://dx.doi.org/10.9708/jksci.2021.26.06.001

Low Resolution Infrared Image Deep Convolution Neural Network for Embedded System  

Hong, Yong-hee (Dept. of PGM Seeker Lab, LIG Nex1)
Jin, Sang-hun (Dept. of PGM Seeker Lab, LIG Nex1)
Kim, Dae-hyeon (Dept. of PGM Seeker Lab, LIG Nex1)
Jhee, Ho-Jin (Dept. of PGM Seeker Lab, LIG Nex1)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.6, 2021 , pp. 1-8 More about this Journal
Abstract
In this paper, we propose reinforced VGG style network structure for low performance embedded system to classify low resolution infrared image. The combination of reinforced VGG style network structure and global average pooling makes lower computational complexity and higher accuracy. The proposed method classify the synthesize image which have 9 class 3,723,328ea images made from OKTAL-SE tool. The reinforced VGG style network structure composed of 4 filters on input and 16 filters on output from max pooling layer shows about 34% lower computational complexity and about 2.4% higher accuracy then the first parameter minimized network structure made for embedded system composed of 8 filters on input and 8 filters on output from max pooling layer. Finally we get 96.1% accuracy model. Additionally we confirmed the about 31% lower inference lead time in ported C code.
Keywords
Deep Learning; CNN; VGG; Low Resolution; Infrared; Synthesize Image;
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