[1]WANG Kaixuan,REN Fuji,NI Hongjun,et al.Image amplification for temperature value image based on cyclic cross-correlation coefficient CGAN[J].CAAI Transactions on Intelligent Systems,2022,17(1):32-40.[doi:10.11992/tis.202106036]
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Image amplification for temperature value image based on cyclic cross-correlation coefficient CGAN

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 1 Page number: 32-40 Column: 学术论文—机器学习 Public date: 2022-01-05
Title:
Image amplification for temperature value image based on cyclic cross-correlation coefficient CGAN
Author(s):
WANG Kaixuan1; 2;  REN Fuji2;  NI Hongjun1;  LYU Shuaishuai1;  WANG Xingxing1
1. School of Mechanical Engineering, Nantong University, Nantong 226019, China;
2. Department of Intelligent Information Engineering, Tokushima University, Tokushima 7708501, Japan
Keywords:
infrared image; image amplification; cyclic cross-correlation coefficient; conditional generative adversarial network; convolution neural network; substation equipment; loss function; image processing; temperature recognition
CLC:
TP181
DOI:
10.11992/tis.202106036
Abstract:
To solve the problems of small sample size and imbalance of infrared image for substation equipment, a temperature image amplification method based on cyclic cross-correlation coefficient conditional generative adversarial network (CGAN) is proposed. The cyclic cross-correlation coefficient is proposed according to the image similarity, which improves the loss function of CGAN. Then the improved CGAN is used to amplify the original temperature image data set, establishing a new data set containing 11 labels. Then, the traditional image amplification method, the original CGAN and the improved CGAN are compared using the convolution neural network (CNN). The experiment demonstrate that the proposed CGAN model has faster convergence speed and stable training process, and the generated images have clear contour and rich details. The objective evaluation index of the proposed method is the largest, and the recognition accuracy of temperature value reaches 99.4%, which realizes the purpose of the image amplification.
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