基于红外成像的海上搜救技术研究
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摘要
及时有效的海上搜救工作可以最大限度地减少事故损失,保障人民生命财产安全。为保证搜救工作的顺利进行,首要条件是快速准确地发现目标。红外热成像系统由于其独特的优点,在目标自动检测、识别和跟踪方面有了越来越多的应用,因此也成为今后海上目标搜救的主要发展方向。但是,现有的红外成像系统不具备目标自动识别功能,仅依靠人眼对红外视频图像进行目标检测,在船体颠簸、红外图像对比度低的情况下,极易造成视觉疲劳,丢失目标。因此,设计出一套适用于海上搜救的红外目标识别系统具有巨大的意义。
本文分析了热红外图像的产生机理、系统特性以及不同波段红外辐射的成像特点。通过编程仿真对多种图像预处理算法进行对比,得出了不同算法的优缺点,并据此选择了自适应门限滤波法作为海上搜救系统的图像预处理算法。针对海上搜救中小目标红外图像信噪比低,且易受到背景杂波干扰等特点,采用了一种基于海上小目标的检测方法,该方法的特点是:首先使用自适应门限滤波来平滑背景,以减少背景噪声的干扰并对目标增强;然后采用图像局部熵函数检测出目标所在位置,这其中使用了遗传算法对函数进行优化,提高了检测效率。最后设计了基于DSP的图像处理硬件电路,使用以上算法实现海上红外小目标的实时检测。
通过对标准中波段红外图像的仿真实验及数据分析,结果表明,该方法能够准确识别出目标,达到了预期效果,具有较高的实用性。
Fast and effectual salvage can reduce loss in accident; ensure people's lives and belongings safety when shipwreck occurs. It is very important that discover targets betimes and exactly to insure the salvage going on wheels. Because of its unique advantages, infrared imaging systems have been more and more applied on targets automatic detecting, identifying and tracking, it will become main developing direction on salvage. However, existing infrared imaging system does not have the function of automatic recognition, just recognize targets by human eyes. In the condition of ship bumping and low contrast infrared images, this will result in visual tired and lost targets easily. Therefore, it is significant that design an infrared targets recognition system suitable for salvage.
This paper analysis principle, nature and imaging character of different wave band infrared images. It contrasts several pre-treatment algorithms by simulating programming, obtains advantages and disadvantages of different algorithms. Against salvage characters of low image SNR and being easy interfered by background clutter, this paper uses a recognition method based on small targets: first smoothes background by adaptive threshold filtering for reducing disturbing of noise, then recognizes targets by image local entropy function. In the processes of recognition, uses genetic algorithms optimizing local entropy function to enhance searching efficiency. At last, designs hardware circuit for images processing based on DSP, and realizes real-time recognition on small infrared targets on the sea with above methods.
According simulating experiment and data analysis on standard media-wave infrared images, obtains the results that this method can recognize targets effective, achieve the desired objectives, have higher utility.
本文分析了热红外图像的产生机理、系统特性以及不同波段红外辐射的成像特点。通过编程仿真对多种图像预处理算法进行对比,得出了不同算法的优缺点,并据此选择了自适应门限滤波法作为海上搜救系统的图像预处理算法。针对海上搜救中小目标红外图像信噪比低,且易受到背景杂波干扰等特点,采用了一种基于海上小目标的检测方法,该方法的特点是:首先使用自适应门限滤波来平滑背景,以减少背景噪声的干扰并对目标增强;然后采用图像局部熵函数检测出目标所在位置,这其中使用了遗传算法对函数进行优化,提高了检测效率。最后设计了基于DSP的图像处理硬件电路,使用以上算法实现海上红外小目标的实时检测。
通过对标准中波段红外图像的仿真实验及数据分析,结果表明,该方法能够准确识别出目标,达到了预期效果,具有较高的实用性。
Fast and effectual salvage can reduce loss in accident; ensure people's lives and belongings safety when shipwreck occurs. It is very important that discover targets betimes and exactly to insure the salvage going on wheels. Because of its unique advantages, infrared imaging systems have been more and more applied on targets automatic detecting, identifying and tracking, it will become main developing direction on salvage. However, existing infrared imaging system does not have the function of automatic recognition, just recognize targets by human eyes. In the condition of ship bumping and low contrast infrared images, this will result in visual tired and lost targets easily. Therefore, it is significant that design an infrared targets recognition system suitable for salvage.
This paper analysis principle, nature and imaging character of different wave band infrared images. It contrasts several pre-treatment algorithms by simulating programming, obtains advantages and disadvantages of different algorithms. Against salvage characters of low image SNR and being easy interfered by background clutter, this paper uses a recognition method based on small targets: first smoothes background by adaptive threshold filtering for reducing disturbing of noise, then recognizes targets by image local entropy function. In the processes of recognition, uses genetic algorithms optimizing local entropy function to enhance searching efficiency. At last, designs hardware circuit for images processing based on DSP, and realizes real-time recognition on small infrared targets on the sea with above methods.
According simulating experiment and data analysis on standard media-wave infrared images, obtains the results that this method can recognize targets effective, achieve the desired objectives, have higher utility.
引文
[1]陈明东.海上搜寻技术研究:(硕士学位论文).大连:大连海事大学,2007.
[2]王应桥.功能完备的航海模拟器中GMDSS模块的研究:(硕士学位论文).大连:大连海事大学,2005.
[3]伏强,胡步发.一种雷达目标识别方法及其实验研究.现代电子.1994,43(2):48-55.
[4]于海霞.AIS与雷达数据融合方法的研究:(硕士学位论文).大连:大连海事大学,2006.
[5]周立伟.目标探测与识别.北京:北京理工大学出版社,2002.
[6]张幼文.红外光学工程.上海:上海科学技术出版时,1982.
[7]王锦昌.夜视照明光源.红外技术.1998,01(20):27-29.
[8]Deny Bonnier,Vincent Larochelle.A range-gated active imaging system for search and rescue,and surveillance operations.SPIE.1996,2744:134-145.
[9]Kevin J.Snell,Andre Parent,Mare Levesque,et al.An active range-gated near IR TV system for all weather surveillance.SPIE.1997,2935:171-181.
[10]David A.Freiwald,Joyce Freiwald.Range-gated laser and ICCD camera system for on-orbit detection of small space debris.SPIE.1994,2214:116-123.
[11]张建奇.红外物理.西安:西安电子科技大学出版社,2004.
[12]张敬贤,李玉丹,金伟其.微光与红外成像技术.北京:北京理工大学出版社,1995.
[13]吴宗凡.红外热像仪的原理和技术发展.光学及光电仪器.1997,02(2):28-30.
[14]Alain Manissandjian,Philippe Tribolet,Philippe Chorier,et al.Sofradir Infrared Detector Products:the Past and the Future.SPIE.2000,4130:480-495.
[15]Pieter A.Jacobs.Thermal Infrared Characterization of Ground Targets and Backgrounds.SPIE Publications,2006
[16]吴北婴,李卫,陈洪滨等.大气传输实用算法.北京:气象出版社,1998.
[17]张雷.双波段红外图像目标特性分析及其融合方法研究:(硕十论文).太原:中北大学,2007.
[18]A.Goldberg,T.Fischer,S.Kennedy.Dual-Band Imaging of Military Targets Using a QWIP Focal Plane Array.Army Research Laboratory,Adelphi,MD 20783.
[19]王江安,肖伟岸.基于双波段的目标红外辐射特征分析.激光与红外,2001,12(3):351-353.
[20]吴军辉,郜竹香,张玉竹.红外成像系统图像空间噪声分析与估计.红外技术,2001,23(3):19-25.
[21]邹前进,冯亮,汪亚.红外图像空间噪声分析和预处理方法改进.应用光学,2007,28(4):426-430.
[22]Kyungim Baek,Bruce A.Draper.Factors Analysis for Background Suppression.IEEE,2002.
[23]惠建江,刘朝晖,刘文.红外图像的噪声分析和弱小目标的增强.红外技术,2005,27(2):135-138.
[24]缪德超.复杂海空背景下红外舰船小目标检测技术研究:(硕士学位论文).哈尔滨:哈尔滨工程大学,2007.
[25]张盈.热成像系统的噪声.红外技术,2005,25(2):33-36.
[26]岳冬青,李燕兰.红外探测器的1/f噪声谱测试.红外与激光工程,2001,30(2):155-156.
[27]Tom V.T,Peli T,Leung M,et al.Morphology-based algorithm for point target detection in infrared backgrounds.SPIE,1993,1954:2-11.
[28]Barnett J.Statistical analysis of median subtraction filtering with application to point target detection in infrared backgrounds.SPIE,1989,1050:10-18.
[29]温佩芝,史泽林,于海斌.基于小波变换的复杂海面背景红外小目标检测.激光与红外.2003,33(6):449-452.
[30]彭嘉雄,周文琳.红外背景抑制与小目标分割检测.电子学报,1999,27(12):47-51.
[31]Scharfand L.L,Friedlander B.Matched subspace detectors.IEEE Trans.On Sig.Proc.1994,42(8):2146-2156.
[32]Kotropoulos C,Pitas I.Adaptive LMS L-Filters for Noise Suppression in Images.IEEE Transactions on Image Processing,1996,5(12):1596-1609.
[33]王新增,严国莉.基于自适应门限滤波的红外弱小运动目标检测方法.红外,2006,27(8):13-15.
[34]李俊山,曹素平,谭园园.基于数学形态学的红外运动小目标识别算法研究.电光与控制.2006,13(2):11-14.
[35]Holland J H.Adaptation in nature and Artificial Systems.MIT Press,1992.
[36]周明,孙树栋.遗传算法原理及应用.北京:国防工业出版,1999.
[37]G.Rawlins.Foundations of Genetic Algorithm.Morgan Kaufmann,1991.
[38]莫鸿强.遗传算法搜索能力和编码方式研究:(硕士学位论文).广州:华南理工大学,2001.
[39]尚飞.遗传算法在图像处理中的应用研究:(硕士学位论文).北京:东北电力大学,2007.
[40]J.J.Grefenstette.Optimization of control parameters for genetic algorithms.IEEE Trans.on Systems Man and Cybernetics,1986,16(1):122-128.
[41]刘松涛,周晓东,王成刚.复杂海空背景下鲁棒的海天线检测算法研究.光电工程.2006,33(8):5-10.
[42]钟都都,闫杰.基于灰色关联分析和Canny算子的图像边缘提取算法.计算机工程与应用.2006,28:68-71.
[43]温佩芝,史泽林,于海滨等.基于小波变换的海面背景红外小目标检测方法.光电工程,2004,31(4):38-41.
[44]谢红,刘玲,刘艳艳.复杂海天线区域检测算法研究.应用科技.2006,33(6):96-98.
[45]梁华为.直接从双峰直方图确定二值化阈值.中国图形图象学报.2002,15(2):253-255.
[46]张淑艳.海天背景下弱小目标实时捕获跟踪研究:(硕士学位论文).长春:中国科学院研究生院(长春光学精密机械与物理研究所),2006.
[47]周霖.DSP信号处理技术应用.北京:国防工业出版社,2004.
[48]张雄伟,陈亮,徐光辉.DSP芯片的原理与开发应用.北京:电子工业出版社,2002.
[49]SAA7111 Video Input Processor Data Sheet Philips Corporation,1998.
[50]TMS320VC5416 Fixed-Point Digital Signal Processor,Data Manual.TI Corporation,2003.
[51]MAX 3000A Programmable Logic Device Family Data Sheet.Altera Corporation,2003.
[2]王应桥.功能完备的航海模拟器中GMDSS模块的研究:(硕士学位论文).大连:大连海事大学,2005.
[3]伏强,胡步发.一种雷达目标识别方法及其实验研究.现代电子.1994,43(2):48-55.
[4]于海霞.AIS与雷达数据融合方法的研究:(硕士学位论文).大连:大连海事大学,2006.
[5]周立伟.目标探测与识别.北京:北京理工大学出版社,2002.
[6]张幼文.红外光学工程.上海:上海科学技术出版时,1982.
[7]王锦昌.夜视照明光源.红外技术.1998,01(20):27-29.
[8]Deny Bonnier,Vincent Larochelle.A range-gated active imaging system for search and rescue,and surveillance operations.SPIE.1996,2744:134-145.
[9]Kevin J.Snell,Andre Parent,Mare Levesque,et al.An active range-gated near IR TV system for all weather surveillance.SPIE.1997,2935:171-181.
[10]David A.Freiwald,Joyce Freiwald.Range-gated laser and ICCD camera system for on-orbit detection of small space debris.SPIE.1994,2214:116-123.
[11]张建奇.红外物理.西安:西安电子科技大学出版社,2004.
[12]张敬贤,李玉丹,金伟其.微光与红外成像技术.北京:北京理工大学出版社,1995.
[13]吴宗凡.红外热像仪的原理和技术发展.光学及光电仪器.1997,02(2):28-30.
[14]Alain Manissandjian,Philippe Tribolet,Philippe Chorier,et al.Sofradir Infrared Detector Products:the Past and the Future.SPIE.2000,4130:480-495.
[15]Pieter A.Jacobs.Thermal Infrared Characterization of Ground Targets and Backgrounds.SPIE Publications,2006
[16]吴北婴,李卫,陈洪滨等.大气传输实用算法.北京:气象出版社,1998.
[17]张雷.双波段红外图像目标特性分析及其融合方法研究:(硕十论文).太原:中北大学,2007.
[18]A.Goldberg,T.Fischer,S.Kennedy.Dual-Band Imaging of Military Targets Using a QWIP Focal Plane Array.Army Research Laboratory,Adelphi,MD 20783.
[19]王江安,肖伟岸.基于双波段的目标红外辐射特征分析.激光与红外,2001,12(3):351-353.
[20]吴军辉,郜竹香,张玉竹.红外成像系统图像空间噪声分析与估计.红外技术,2001,23(3):19-25.
[21]邹前进,冯亮,汪亚.红外图像空间噪声分析和预处理方法改进.应用光学,2007,28(4):426-430.
[22]Kyungim Baek,Bruce A.Draper.Factors Analysis for Background Suppression.IEEE,2002.
[23]惠建江,刘朝晖,刘文.红外图像的噪声分析和弱小目标的增强.红外技术,2005,27(2):135-138.
[24]缪德超.复杂海空背景下红外舰船小目标检测技术研究:(硕士学位论文).哈尔滨:哈尔滨工程大学,2007.
[25]张盈.热成像系统的噪声.红外技术,2005,25(2):33-36.
[26]岳冬青,李燕兰.红外探测器的1/f噪声谱测试.红外与激光工程,2001,30(2):155-156.
[27]Tom V.T,Peli T,Leung M,et al.Morphology-based algorithm for point target detection in infrared backgrounds.SPIE,1993,1954:2-11.
[28]Barnett J.Statistical analysis of median subtraction filtering with application to point target detection in infrared backgrounds.SPIE,1989,1050:10-18.
[29]温佩芝,史泽林,于海斌.基于小波变换的复杂海面背景红外小目标检测.激光与红外.2003,33(6):449-452.
[30]彭嘉雄,周文琳.红外背景抑制与小目标分割检测.电子学报,1999,27(12):47-51.
[31]Scharfand L.L,Friedlander B.Matched subspace detectors.IEEE Trans.On Sig.Proc.1994,42(8):2146-2156.
[32]Kotropoulos C,Pitas I.Adaptive LMS L-Filters for Noise Suppression in Images.IEEE Transactions on Image Processing,1996,5(12):1596-1609.
[33]王新增,严国莉.基于自适应门限滤波的红外弱小运动目标检测方法.红外,2006,27(8):13-15.
[34]李俊山,曹素平,谭园园.基于数学形态学的红外运动小目标识别算法研究.电光与控制.2006,13(2):11-14.
[35]Holland J H.Adaptation in nature and Artificial Systems.MIT Press,1992.
[36]周明,孙树栋.遗传算法原理及应用.北京:国防工业出版,1999.
[37]G.Rawlins.Foundations of Genetic Algorithm.Morgan Kaufmann,1991.
[38]莫鸿强.遗传算法搜索能力和编码方式研究:(硕士学位论文).广州:华南理工大学,2001.
[39]尚飞.遗传算法在图像处理中的应用研究:(硕士学位论文).北京:东北电力大学,2007.
[40]J.J.Grefenstette.Optimization of control parameters for genetic algorithms.IEEE Trans.on Systems Man and Cybernetics,1986,16(1):122-128.
[41]刘松涛,周晓东,王成刚.复杂海空背景下鲁棒的海天线检测算法研究.光电工程.2006,33(8):5-10.
[42]钟都都,闫杰.基于灰色关联分析和Canny算子的图像边缘提取算法.计算机工程与应用.2006,28:68-71.
[43]温佩芝,史泽林,于海滨等.基于小波变换的海面背景红外小目标检测方法.光电工程,2004,31(4):38-41.
[44]谢红,刘玲,刘艳艳.复杂海天线区域检测算法研究.应用科技.2006,33(6):96-98.
[45]梁华为.直接从双峰直方图确定二值化阈值.中国图形图象学报.2002,15(2):253-255.
[46]张淑艳.海天背景下弱小目标实时捕获跟踪研究:(硕士学位论文).长春:中国科学院研究生院(长春光学精密机械与物理研究所),2006.
[47]周霖.DSP信号处理技术应用.北京:国防工业出版社,2004.
[48]张雄伟,陈亮,徐光辉.DSP芯片的原理与开发应用.北京:电子工业出版社,2002.
[49]SAA7111 Video Input Processor Data Sheet Philips Corporation,1998.
[50]TMS320VC5416 Fixed-Point Digital Signal Processor,Data Manual.TI Corporation,2003.
[51]MAX 3000A Programmable Logic Device Family Data Sheet.Altera Corporation,2003.
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