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http://dx.doi.org/10.33778/kcsa.2020.20.2.191

Design of detection method for smoking based on Deep Neural Network  

Lee, Sanghyun (한국과학기술원 정보보호대학원)
Yoon, Hyunsoo (한국과학기술원 정보보호대학원)
Kwon, Hyun (육군사관학교 전자공학과)
Publication Information
Convergence Security Journal / v.21, no.1, 2021 , pp. 191-200 More about this Journal
Abstract
Artificial intelligence technology is developing in an environment where a lot of data is produced due to the development of computing technology, a cloud environment that can store data, and the spread of personal mobile phones. Among these artificial intelligence technologies, the deep neural network provides excellent performance in image recognition and image classification. There have been many studies on image detection for forest fires and fire prevention using such a deep neural network, but studies on detection of cigarette smoking were insufficient. Meanwhile, military units are establishing surveillance systems for various facilities through CCTV, and it is necessary to detect smoking near ammunition stores or non-smoking areas to prevent fires and explosions. In this paper, by reflecting experimentally optimized numerical values such as activation function and learning rate, we did the detection of smoking pictures and non-smoking pictures in two cases. As experimental data, data was constructed by crawling using pictures of smoking and non-smoking published on the Internet, and a machine learning library was used. As a result of the experiment, when the learning rate is 0.004 and the optimization algorithm Adam is used, it can be seen that the accuracy of 93% and F1-score of 94% are obtained.
Keywords
Smoking detection; Machine learning; Detection method; Neural network;
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