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http://dx.doi.org/10.22156/CS4SMB.2021.11.12.030

Thermal Image Processing and Synthesis Technique Using Faster-RCNN  

Shin, Ki-Chul (Dept. of Electronic Computer Engineering, Inha University)
Lee, Jun-Su (Dept. of Mechanical Engineering, Inha University)
Kim, Ju-Sik (Dept. of Mechanical Engineering, Inha University)
Kim, Ju-Hyung (Dept. of Digital Solution Section Hydro&Nuclear Power Company)
Kwon, Jang-woo (Dept. of Electronic Computer Engineering, Inha University)
Publication Information
Journal of Convergence for Information Technology / v.11, no.12, 2021 , pp. 30-38 More about this Journal
Abstract
In this paper, we propose a method for extracting thermal data from thermal image and improving detection of heating equipment using the data. The main goal is to read the data in bytes from the thermal image file to extract the thermal data and the real image, and to apply the composite image obtained by synthesizing the image and data to the deep learning model to improve the detection accuracy of the heating facility. Data of KHNP was used for evaluation data, and Faster-RCNN is used as a learning model to compare and evaluate deep learning detection performance according to each data group. The proposed method improved on average by 0.17 compared to the existing method in average precision evaluation.As a result, this study attempted to combine national data-based thermal image data and deep learning detection to improve effective data utilization.
Keywords
Image Fusion; Deep Learning; Thermal Image; Image Processing; Abnormal Diagnosis;
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