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论文:2017,Vol:35,Issue(5):910-914 |
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引用本文： |
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胡磊力, 张君昌, 张亮中. 基于人类视觉系统的实时红外目标检测方法[J]. 西北工业大学学报 |
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Hu Leili, Zhang Junchang, Zhang Liangzhong. Real-Time Infrared Target Detection Method Based on Human Vision System[J]. Northwestern polytechnical university |
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| 基于人类视觉系统的实时红外目标检测方法 |
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| 胡磊力1, 张君昌1,2, 张亮中2 |
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1. 中航工业洛阳电光设备研究所, 河南 洛阳 471009;
2. 西北工业大学 电子信息学院, 陕西 西安 710129 |
| 摘要: |
| 针对现有基于人类视觉系统红外目标检测方法存在的实时性问题，提出一种融合显著区域提取和局部对比度分析的红外目标检测新方法。首先，采用快速中值滤波和灰度合并得到二值图像并进行融合，以提高显著区域提取的精度；其次，对融合后的二值图像进行连通域分析，过滤连通区域过大和过小的区域，以减少后续检测方法的计算量；然后，在显著区域所对应的原图区域进行局部对比度分析，得到区域局部对比度图，并进行阈值分割得到弱小目标所在位置。理论分析和仿真结果表明该方法在保证检测性能的同时减少了数据运算量和储存量，提高系统的实时性。 |
| 关键词:
红外弱小目标
显著区域
局部对比度
计算效率
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| Real-Time Infrared Target Detection Method Based on Human Vision System |
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| Hu Leili1, Zhang Junchang1,2, Zhang Liangzhong2 |
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1. Department of Luoyang Institute of Electro-Optical Devices, Luoyang 471009, China;
2. Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China |
| Abstract: |
| Focusing on the real-time performance problem of existing infrared target detecting method based on human visual system, a new robust infrared target detecting method that fuses salient region extraction and local contrast analysis was proposed in this paper. Firstly, the binary image was obtained by the fast-median filtering and similarity analysis to enhance the salient region extracting accuracy. Secondly, the too large and too small area was filtered by connectivity domain analysis in binary image to reduce the amount of calculation subsequent detection algorithm. Finally, the regional local contrast diagram was obtained by local contrast analysis in significant intraregional, and the position of the dim small target was obtained by dividing threshold value. Theoretical analysis and simulation results show that the proposed method not only can ensure detection performance, but also reduce the amount of computation and data storage capacity, and improve the real-time performance of the system simultaneously. The SNR gain is up to 9.1 dB when infrared image SNR is lower. |
| Key words:
infrared dim small target
salient region
local contrast
computational efficiency
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| 收稿日期: 2016-12-20
修回日期:
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| DOI: |
| 基金项目: 光电控制技术重点实验室和航空科学基金（2016515303）资助 |
| 通讯作者:
Email： |
| 作者简介: 胡磊力(1973-),中航工业洛阳电光设备研究所研究员,主要从事光电探测研究。
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参考文献: |
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[1] Bai X, Zhou F. Analysis of New Top-Hat Transformation and the Application for Infrared Dim Small Target Detection[J]. Pattern Recognition, 2010, 43:2145-2156
[2] 孙继刚. 序列图像红外小目标检测与跟踪算法研究[D]. 长春:中国科学院大学, 2014 Sun Jigang. Research on Algorithm of Infrared Small Target Detection and Tracking in Image Sequence[D]. Changchun, University of Chinese Academy of Sciences, 2014(in Chinese)
[3] Chen Philip, Li Hong, Wei Yantao, et al. A Local Contrast Method for Small Infrared Target Detection[J]. IEEE Trans on Geoscience And Remote Sensing, 2014, 52(1):574-581
[4] Han Jinhui, Ma Yong, Zhou Bo, et al. A Robust Infrared Small Target Detection Algorithm Based on Human Visual System[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(12):2168-2172
[5] 王晓阳. 局部对比度结合区域显著性红外弱小目标检测[J]. 强激光与粒子束, 2015, 27(9):091005 Wang Xiaoyang. Infrared Small Dim Target Detection Based on Local Contrast Combined with Region Saliency[J]. High Power Laser and Particle Beams, 2015, 27(9):091005(in Chinese) |
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