[1]闵超波,顾 燕,杨 锋.基于泊松分布的日盲紫外电晕检测[J].红外技术,2020,42(8):715-721.[doi:doi:10.11846/j.issn.1001_8891.202008002]
 MIN Chaobo,GU Yan,YANG Feng.Corona Detection of Solar-Blind Ultraviolet via Poisson Distribution[J].Infrared Technology,2020,42(8):715-721.[doi:doi:10.11846/j.issn.1001_8891.202008002]
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基于泊松分布的日盲紫外电晕检测
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
42卷
期数:
2020年第8期
页码:
715-721
栏目:
出版日期:
2020-08-23

文章信息/Info

Title:
Corona Detection of Solar-Blind Ultraviolet via Poisson Distribution
文章编号:
1001-8891(2020)08-0715-07
作者:
闵超波1顾 燕2杨 锋2
1. 河海大学常州校区 物联网工程学院,江苏 常州 213000;2. 北方夜视技术股份有限公司,江苏 南京 211102
Author(s):
MIN Chaobo1GU Yan2YANG Feng2
1. College of Internet of Things Engineering, Hohai University, Changzhou 213000, China;
2. North Night Vision Technology Corp., Ltd., Nanjing 211102, China
关键词:
日盲紫外电晕探测目标检测泊松分布
Keywords:
solar-blind ultraviolet corona detection object detection Poisson distribution
分类号:
TP391
DOI:
doi:10.11846/j.issn.1001_8891.202008002
文献标志码:
A
摘要:
针对日盲紫外电晕探测噪声大、信号微弱等特点,本文提出了一种利用泊松分布的日盲紫外电晕检测方法。为了表征紫外电晕信号的时空域特性,根据光电探测原理,构建了基于泊松分布的紫外电晕目标检测模型。针对紫外图像序列,首先根据暗噪声统计模型进行有无信号的判断,然后利用所建立的紫外电晕目标检测模型得到紫外图像的泊松概率映射图,最后基于改进的最大类间方差法(Otsu)实现紫外电晕目标检测与提取。本文方法在多组紫外图像序列中进行测试与对比,实验证明该方法可以准确地检测出紫外图像中电晕目标,方法结构简单,检测精度高,且性能鲁棒。
Abstract:
A novel model is proposed for the object detection of solar-blind ultraviolet corona using the Poisson distribution and based on the photoelectric detection theory. This model can be used to represent the spatiotemporal characterization of solar-blind ultraviolet corona. In ultraviolet image sequences, initially, the existence of ultraviolet corona is determined by the statistical model of dark noise. Subsequently, the Poisson distribution map of the ultraviolet image is obtained using the proposed model. Finally, an improved Otsu’s method is applied to detect and extract the object of ultraviolet corona. The proposed method is successfully tested over many ultraviolet image sequences and compared with the other two methods. The experiment results demonstrate that the proposed method performs better in object detection and has higher accuracy in ultraviolet image sequences.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2020-05-25;修订日期:2020-06-23.
作者简介:闵超波(1987-),江苏常州人,博士,讲师,研究方向:机器视觉与光电探测。E-mail:chaobomin@outlook.com。
基金项目:国家自然科学基金(61901157)。
更新日期/Last Update: 2020-08-19