背景约束的红外复杂背景下坦克目标分割方法

高敏; 李怀胜; 周玉龙; 方丹; 张宝全

doi:10.16383/j.aas.2016.c150492

背景约束的红外复杂背景下坦克目标分割方法

doi: 10.16383/j.aas.2016.c150492

高敏^1,,
李怀胜^2,,
周玉龙^1,3, ,,
方丹^1,,
张宝全^4,

1.
军械工程学院导弹工程系石家庄 050003
2.
66393部队装备部保定 071002
3.
66393部队博士后科研工作站保定 071002
4.
国网河北省电力公司检修分公司石家庄 050071

基金项目:

中国博士后科学基金 2014M562657

详细信息

作者简介:
高敏   军械工程学院导弹工程系教授, 66393部队博士后科研工作站合作导师.1992年获北京理工大学电子工程专业博士学位.主要研究方向为精确制导技术.E-mail:Gaomin@126.com

李怀胜   66393部队博士后科研工作站合作导师.主要研究方向为装备维修保障.E-mail:lihuaisheng11@126.com

方丹   军械工程学院导弹工程系讲师, 2013年获军械工程学院导弹工程系博士学位.主要研究方向为精确制导技术.E-mail:aq_fd@126.com

张宝全   国网河北省电力公司检修分公司工程师, 2008年获华北电力大学计算机科学与技术本科学历.主要研究方向为红外测温、红外成像检测.E-mail:18031168801@163.com

通讯作者:
周玉龙 66393部队博士后科研工作站在站博士后.2012年获军械工程学院光学与电子工程系博士学位.主要研究方向为红外成像制导技术中的目标分割、识别及跟踪.本文通信作者.E-mail:zyljq@126.com

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出版历程
- 收稿日期: 2015-07-30
- 录用日期: 2015-12-03
- 刊出日期: 2016-03-20

Tank Segmentation Under Infrared Complex Background with Background Restriction

GAO Min^1
,,
LI Huai-Sheng^2
,,
ZHOU Yu-Long^{1,3
, ,},
FANG Dan^1
,,
ZHANG Bao-Quan^4
,

1.
Missile Engineering Department of Ordnance Engineering College, Shijiazhuang 050003
2.
66393 Troops Equipment Department, Baoding 071002
3.
66393 Troops Postdoctoral Science Research Workstation, Baoding 071002
4.
State Grid Hebei Electronic Power Company Maintenance Branch, Shijiazhuang 050071

Funds:

Postdoctoral Science Foundation of China 2014M562657

More Information

Author Bio:
  Professor at the Missile Engineering Department, Ordnance Engineering College. Postdoctoral tutor at the 66393 Troops Postdoctoral Science Research Workstation. He received his Ph. D. degree in electronic engineering from Beijing Institute of Technology in 1992. His main research interest is precise guidance technology.E-mail:

  Postdoctoral tutor at the 66393 Troops Postdoctoral Science Research Workstation. His main research interest is equipment maintenance.E-mail:

  Lecturer at the Missile Engineering Department, Ordnance Engineering College. He received his Ph. D. degree at the Missile Engineering Department, Ordnance Engineering College in 2013. His main research interest is precise guidance technology.E-mail:

   Engineer at the State Grid Hebei Electric Power Company. He received his bachelor degree in computer science and technology from North China Electric Power University in 2008. His research interest covers infrared temperature measurement, infrared imaging detection.E-mail:

Corresponding author: ZHOU Yu-Long Postdoctor at the 66393 Troops Postdoctoral Science and Research Workstation. He received his Ph. D. degree in optical engineering from Ordnance Engineering College in 2012. His research interest covers infrared target segmentation, recognition and tracking. Corresponding author of this paper.E-mail:zyljq@126.com

摘要

摘要: 为实现自寻的反坦克导弹红外导引头对复杂背景下坦克目标的快速有效分割, 以红外导引头拍摄的坦克目标红外图像为研究对象, 采用计算简单的最大类间方差法, 对其分割效果进行了研究.根据实验结果, 揭示了最大类间方差法进行图像分割的有效性机理.在此基础上, 提出了对背景区域像素和灰度级别进行约束的思想, 以降低背景区域类内方差, 提高算法的分割精度, 并给出了具体的方法.首先利用坦克目标的先验信息, 根据光学成像原理, 推导了红外坦克目标图像的大小估计公式, 用来实现对背景像素的约束; 然后采用黄金分割法对背景灰度级别进行约束; 最后利用最大类间方差法实现了复杂背景下红外坦克目标的分割.实验表明, 本文方法的分割效果堪比手工分割效果, 且计算量较少, 算法耗时最大不超过1.44 ms, 完全满足对坦克目标图像分割的有效性和实时性需求.
- 自寻的反坦克导弹红外成像制导系统 /
- 坦克目标分割 /
- 最大类间方差法 /
- 阈值分析 /
- 背景约束
Abstract: To achieve fast and efficient tank segmentation under infrared complex background for infrared imaging guidance system of homing antitank missile, the maximum between-class variance method is studied using tank infrared images from the infrared imaging and guidance system, and the working mechanism of the maximum between-class variance method is revealed. Then the idea to restrict the background pixels and gray levels is proposed to lower the background within-class variance so as to improve the segmentation accuracy of the method. The detailed approach is as follows. Firstly, with the prior information of the tank, according to the optical imaging principle, the size equation of the tank image is deduced, by which background pixels are restricted; secondly, the golden section is used to restrict the background gray levels; lastly, with the maximum between-class variance method, segmentation of the tank target is achieved. Experiments show that the segmentation result of the proposed method is as good as manual segmentation's and the processing time is decreased to no more than 1.44 ms. So it can completely meet the needs of effectiveness and real-time processing for the tank segmentation under the infrared complex background of the homing antitank missile.
- Infrared imaging guidance system of the homing antitank missile /
- tank segmentation /
- the maximum between-class variance method /
- threshold analysis /
- background restriction

HTML全文

图 1 不同距离的坦克目标红外图像

Fig. 1 Infrared tank images of different distance

下载: 全尺寸图片幻灯片

图 2 灰度直方图

Fig. 2 Gray histograms

下载: 全尺寸图片幻灯片

图 3 Otsu法二值化分割图

Fig. 3 Two-valued results by Otsu method

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图 4 类内方差随灰度级的变化曲线

Fig. 4 The within-class variance curves changed with gray levels

下载: 全尺寸图片幻灯片

图 5 距离为1 455米处的白天坦克红外图像分割结果

Fig. 5 Segmentation results of tank infrared image taken in daytime with distance of 1 455 m

下载: 全尺寸图片幻灯片

图 6 距离为1 199米处的白天坦克红外图像分割结果

Fig. 6 Segmentation results of tank infrared image taken in daytime with distance of 1 199 m

下载: 全尺寸图片幻灯片

图 7 距离为936米处的白天坦克红外图像分割结果

Fig. 7 Segmentation results of tank infrared image taken in daytime with distance of 936 m

下载: 全尺寸图片幻灯片

图 8 距离为573米处的白天坦克红外图像分割结果

Fig. 8 Segmentation results of tank infrared image taken in daytime with distance of 573 m

下载: 全尺寸图片幻灯片

图 9 距离为446米处的白天坦克红外图像分割结果

Fig. 9 Segmentation results of tank infrared image taken in daytime with distance of 446 m

下载: 全尺寸图片幻灯片

图 10 距离为1 611米处夜间坦克红外图像分割结果

Fig. 10 Segmentation results of tank infrared image taken at night with distance of 1 611 m

下载: 全尺寸图片幻灯片

图 11 距离为1 251米处夜间坦克红外图像分割结果

Fig. 11 Segmentation results of tank infrared image taken at night with distance of 1 251 m

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图 12 类距离为980米处夜间坦克红外图像分割结果

Fig. 12 Segmentation results of tank infrared image taken at night with distance of 980 m

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图 13 距离为740米处夜间坦克红外图像分割结果

Fig. 13 egmentation results of tank infrared image taken at night with distance of 740 m

下载: 全尺寸图片幻灯片

图 14 距离为353米处夜间坦克红外图像分割结果

Fig. 14 Segmentation results of tank infrared image taken at night with distance of 353 m

下载: 全尺寸图片幻灯片

表 1 坦克目标距离及所占最大像素数

Table 1 The target distance and its maximum pixels

	Daytime images					Night images
	Fig. 5(a)	Fig. 6(a)	Fig. 7(a)	Fig. 8(a)	Fig. 9(a)	Fig. 10(a)	Fig. 11(a)	Fig. 12(a)	Fig. 13(a)	Fig. 14(a)
Target distance (m)	1455	1199	936	573	446	1 611	1 251	980	740	353
Target pixels	526	775	1 271	3 390	5 601	429	712	1 159	2 032	8 924

下载: 导出CSV

表 2 算法分割精度对比(%)

Table 2 The segmentation accuracy comparison of different methods (%)

	Images	Standard Otsu method	2-D maximum entropy	KFCM	Proposed method
Daytime	Fig. 5(a)	0.25	76.57	85.93	96.01
	Fig. 6(a)	0.26	0.28	98.19	91.54
	Fig. 7(a)	0.90	0.91	97.47	99.69
	Fig. 8(a)	62.08	98.08	94.09	97.08
	Fig. 9(a)	78.68	96.17	89.40	98.51
Night	Fig. 10(a)	0.09	0.09	95.26	99.57
	Fig. 11(a)	71.84	73.26	66.81	86.23
	Fig. 12(a)	0.59	79.35	0.81	99.91
	Fig. 13(a)	0.68	0.67	0.76	99.89
	Fig. 14(a)	7.10	54.93	99.92	96.98
	Average	22.25	48.03	72.86	96.54

下载: 导出CSV

表 3 算法耗时对比

Table 3 The consuming time comparison of different methods

	Images	Standard Otsu method (ms)	2-D maximum entropy (ms)	KFCM (ms)	Proposed method (ms)
Daytime	Fig. 5(a)	1.42	4 260.79	27597.60	1.40
	Fig. 6(a)	1.32	4 247.61	21 529.17	1.27
	Fig. 7(a)	1.55	4 237.53	41 853.46	1.41
	Fig. 8(a)	1.47	4 031.80	54170.71	1.39
	Fig. 9(a)	1.36	4 158.24	52 666.22	1.29
Night	Fig. 10(a)	1.44	4 375.39	21 598.83	1.40
	Fig. 11(a)	1.64	4 521.92	26614.01	1.40
	Fig. 12(a)	1.43	4153.51	27991.33	1.34
	Fig. 13(a)	1.52	3 839.10	43 789.32	1.44
	Fig. 14(a)	1.40	4 671.52	45 569.53	1.32
	Average	1.45	4 249.74	36 338.02	1.37

下载: 导出CSV

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