Nuclear radiation detection based on the convolutional neural network under public surveillance scenarios
Yan Zhangfa,
Zhang Zhaohui,
Xu Shuyu,
Ma Juxiang,
Hou Yansong,
Ji Yingcai,
Sun Lifeng,
Dai Tiantian,
Wei Qingyang
Affiliations
Yan Zhangfa
Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Zhang Zhaohui
Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Xu Shuyu
Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Ma Juxiang
Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Hou Yansong
Beijing Novel Medical Equipment Ltd., Beijing 102206, China
Ji Yingcai
Beijing Novel Medical Equipment Ltd., Beijing 102206, China
Sun Lifeng
CNNC High Energy Equipment (Tianjin) Co., Ltd., Tianjin 300300, China
Dai Tiantian
Department of Radiation Oncology, China-Japan Friendship Hospital, Beijing 100029, China
Wei Qingyang
Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Nuclear energy is a clean and popular form of energy, but leakage and loss of nuclear material pose a threat to public safety. Radiation detection in public spaces is a key part of nuclear security. Common security cameras equipped with complementary metal oxide semiconductor (CMOS) sensors can help with radiation detection. Previous work with these cameras, however, required slow, complex frame-by-frame processing. Building on the previous work, we propose a nuclear radiation detection method using convolution neural networks (CNNs). This method detects nuclear radiation in changing images with much less computational complexity. Using actual video images captured in the presence of a common Tc-99m radioactive source, we construct training and testing sets. After training the CNN and processing our test set, the experimental results show the high performance and effectiveness of our method.
