期刊文献+

基于深度学习的红外序列图像小目标检测方法研究 被引量:6

Research on small target detection method of infrared sequence image based on depth learning
下载PDF
导出
摘要 红外序列图像小目标在环境干扰下检测准确性不好,为了提高红外序列图像小目标检测能力,提出基于深度学习的红外序列图像小目标检测方法。构建红外序列小目标图像的三维成像模型,采用边缘特征分割和角点分布式提取方法进行红外序列小目标图像的多维尺度分解,构建目标图像的三维成像模型,采用灰度信息重构方法进行红外目标图像的像素序列重组,建立目标图像的三维轮廓特征分布集,结合模板特征匹配和超像素特征序列重构方法进行红外序列图像小目标序列重构,采用模糊信息度特征提取方法实现红外序列小目标图像的特征提取和优化检测,构建红外序列小目标图像的像素分布灰度像素集,通过深度学习算法进行红外序列图像小目标检测过程中的自适应寻优,实现红外序列图像小目标检测优化。仿真结果表明,采用该方法进行红外序列图像小目标检测的自适应性较好,特征分辨能力较强,提高了目标的检测精度。 The detection accuracy of infrared sequence image small target under environmental interference is not good.In order to improve the detection ability of infrared sequence image small target,an infrared sequence image small target detection method based on depth learning is proposed.The 3 D imaging model of infrared sequence small target image is constructed,the multi-dimensional scale decomposition of infrared sequence small target image is carried out by using edge feature segmentation and corner distributed extraction method,the 3 D imaging model of target image is constructed,the pixel sequence reconstruction method of infrared target image is used to reconstruct the pixel sequence of infrared target image,and the 3 D profile feature distribution set of target image is established.Combining template feature matching and super pixel feature sequence reconstruction,infrared sequence image small target sequence reconstruction is carried out.Fuzzy information degree feature extraction method is used to realize the feature extraction and optimization detection of infrared sequence small target image.The pixel distributed gray pixel set of infrared sequence small target image is constructed,and the adaptive optimization in the process of infrared sequence image small target detection is carried out by depth learning algorithm.The optimization of infrared sequence image small target detection is realized.The simulation results show that the method has good adaptability and feature resolution,and improves the detection accuracy of infrared sequence images.
作者 周贵华 许丽娟 周伟昌 ZHOU Guihua;XU Lijuan;ZHOU Weichang(Guangzhou University Songtian College,Guangdong 511310,China;Huashang College,Guangdong University of Finance&Economics,Guangdong 511300,China;School of Physics and Electronics,Hunan Normal University 410000,China)
出处 《激光杂志》 北大核心 2020年第12期61-64,共4页 Laser Journal
基金 国家自然科学基金(No.51102091)。
关键词 深度学习 红外序列图像 小目标 检测 像素 depth learning infrared sequence image small target detection pixel
  • 相关文献

参考文献11

二级参考文献53

  • 1傅碧宏,冯筠.合成孔径雷达(SAR)数据在地质研究中的应用[J].遥感信息,1993,15(4):21-22. 被引量:1
  • 2杨东凯,张益强,张其善,李紫薇.基于GPS散射信号的机载海面风场反演系统[J].航空学报,2006,27(2):310-313. 被引量:17
  • 3姚克明,宋利权,张金锁.基于复杂背景的红外机场目标自动识别算法研究[J].红外与激光工程,2007,36(3):398-402. 被引量:8
  • 4Lindeberg T. Feature detection with automatic scale selection[J]. International Journal of Computer Vision, 1998, 30(2): 77-116.
  • 5Krystian Mikolajczyk C S. Indexing based on scale invariant interest points[C]//In Proceedings of the 8th International Conference on Computer Vision. 2001:525-531.
  • 6Lindeberg T. Scale-space encyclopedia of computer science and engineering[M]. (Benjamin Wah, ed), John Wiley and Son, 2008.
  • 7SimDG, KwonOK, ParkRH. Object matching algorithm using robust Hausdorff distance measure[J]. IEEE Trans. Image Process, 1999, 8(2): 425-429.
  • 8Rizvi S A, Nasrabadi N M. Fusion of FLIR automatic target recognition algorithms[J]. Information Fusion, 2003, 4(4): 247-258.
  • 9R. M. Haralick, K. Shanmugam, I. Dinstein. Texture features for image classification [ J ]. IEEE Trans. Syst. Man Cybern, 1973,3:610 - 621.
  • 10F. Crow. Summed-area tables for texture mapping[ J ]. Computer Graphics, Techniques ( SIGRAPH' 84 ). 1984,18 ( 3 ) :207 - 212.

共引文献208

同被引文献60

引证文献6

二级引证文献18

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部