基于视觉图像中电离辐射响应特征的核辐射探测

doi:10.16265/j.cnki.issn1003-3033.2026.02.0619

中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (2): 102-109.doi: 10.16265/j.cnki.issn1003-3033.2026.02.0619

基于视觉图像中电离辐射响应特征的核辐射探测

黄有骏¹(), 游琼宇², 徐守龙²^,^**(), 侯志雄², 彭国文²

¹ 中国核动力研究设计院核反应堆技术全国重点实验室,四川成都 610213
² 南华大学资源环境与安全工程学院,湖南衡阳 421001

收稿日期:2025-09-12 修回日期:2025-12-20 出版日期:2026-02-28
通信作者:
^** 徐守龙(1988—),男,甘肃嘉峪关人,博士,副教授,主要从事核安全探测、监测技术等方面的研究。E-mail:xusl@usc.edu.cn。
作者简介:
黄有骏 (1985—),男,四川成都人,硕士,高级工程师,主要从事反应堆核辐射探测方面的研究。E-mail:drsi-d404-hyj@npic.ac.cn。
彭国文, 教授;
基金资助:
国家自然科学基金资助(11905102); 核反应堆技术全国重点实验室运行基金资助(LRSDT12023302)

A nuclear environment monitoring method based on ionizing radiation response characteristics in visual images

HUANG Youjun¹(), YOU Qiongyu², XU Shoulong²^,^**(), HOU Zhixiong², PENG Guowen²

¹ National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu Sichuan 610213, China
² School of Resources, Environment and Safety Engineering, University of South China, Hengyang Hunan 421001, China

Received:2025-09-12 Revised:2025-12-20 Published:2026-02-28

摘要/Abstract

摘要：

为实时识别动态变化环境中的辐射源,提出一种基于视觉图像中电离辐射响应特征的核环境监测方法。通过试验分析电离辐射对视觉图像的响应特征,包括像素值的均值、方差、偏度和峰度等统计参数,量化特征数据与辐射剂量率的关系,验证拟合的高精度线性相关性;利用二维小波包分解分析视频图像中高频分量,提出一种提取复杂背景中辐射响应信号的算法,精准监测核环境中γ射线辐射剂量率。试验结果表明:γ射线辐射剂量率在51.61~479.24 Gy/h范围内,响应事件数量与剂量率呈显著线性关系,拟合曲线的相关系数分别达到0.998 9和0.999 3;在像素响应特征方面,均值、方差随剂量率增大显著增加,而偏度与峰度表现为指数下降趋势;当像素值阈值为130时,拟合结果的线性度最佳;通过二维小波包分解,抑制了复杂背景干扰,实现了在复杂工况下对剂量率的稳定表征。

关键词: 视觉图像, 电离辐射, 辐射响应, 辐射探测, γ射线, 剂量率, 像素传感器

Abstract:

This article proposes a nuclear environment monitoring method based on ionizing radiation response characteristics in visual images. Through experimental analysis of the response characteristics of ionizing radiation to visual images, including statistical parameters such as mean, variance, skewness, and kurtosis of pixel values, the relationship between feature data and radiation dose rate was quantified, and the high-precision linear correlation of the fitting was verified. A two-dimensional wavelet packet decomposition is used to analyze high-frequency components in video images, and an algorithm is proposed to extract radiation response signals from complex backgrounds, achieving accurate monitoring of gamma ray radiation dose rates in nuclear environments. The experimental results indicate that within the gamma ray radiation dose rate range of 51.61 Gy/h to 479.24 Gy/h, there is a significant linear relationship between the number of response events and the dose rate. The correlation coefficients of the fitted curves are 0.998 9 and 0.999 3, respectively. In terms of pixel response characteristics, the mean and variance significantly increase with increasing dose rate, while skewness and kurtosis show an exponential downward trend. In addition, the experiment verified the influence of setting the pixel value threshold on radiation dose rate measurement. When the pixel value threshold is 130, the linearity of the fitting results is optimal. By using two-dimensional wavelet packet decomposition, the statistical analysis of high-frequency components in the image has further improved the accuracy of dose rate characterization, especially achieving maximum linearity in the diagonal components. This method can efficiently extract radiation response information in complex environments, achieve precise monitoring of gamma ray dose rates in nuclear environments, and provide technical support for emergency response to nuclear leaks and assessment of radioactive areas.

Key words: visual image, ionizing radiation, radiation response, radiation detection, gamma ray, dose rate, pixel sensor

中图分类号:

X945

黄有骏, 游琼宇, 徐守龙, 侯志雄, 彭国文. 基于视觉图像中电离辐射响应特征的核辐射探测[J]. 中国安全科学学报, 2026, 36(2): 102-109.

HUANG Youjun, YOU Qiongyu, XU Shoulong, HOU Zhixiong, PENG Guowen. A nuclear environment monitoring method based on ionizing radiation response characteristics in visual images[J]. China Safety Science Journal, 2026, 36(2): 102-109.

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基于视觉图像中电离辐射响应特征的核辐射探测

A nuclear environment monitoring method based on ionizing radiation response characteristics in visual images

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