小目标检测识别技术性能评价研究
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摘要
小目标检测识别是自动目标识别的重要组成部分和研究方向。小目标的检测和跟踪问题产生于远程监控警戒的应用背景中。近些年来,强杂波条件下的小目标的检测研究工作已愈来愈为人们所重视,对大量已形成或正形成的小目标检测识别算法和系统进行有效的评估、并通过性能评估来改进算法和发展新算法成为日益迫切的需求。小目标检测识别技术性能评价对研究和发展小目标检测识别算法与系统、界定算法与系统的边界与适应性、提高系统的性能十分关键。
本论文工作的目的是结合国家自然科学基金重点项目和国防重点预研课题,研究小目标检测识别算法性能评价的理论、方法与支撑环境模型,为算法与系统的研制提供理论依据和试验支撑。因此,本文拟从三方面展开研究:一是目标检测识别算法性能评价的基本框架研究;二是小目标检测识别算法性能评价新方法研究,包括机理分析方法、基于试验设计方法学的性能评价方法、全流程性能评价技术等,用以评估现有算法或支撑发展新算法;三是小目标检测识别算法研究与性能评价支撑环境理论模型及其实现技术的研究。为小目标检测识别技术研究一体化提供支持。
本文通过对目标检测识别算法性能评估现状的分析,提出了将算法研究与性能评估过程统一到一个框架的观点。论述了性能评估的基本原理和方法,把系统辨识模型引入到算法系统性能的建模中,发展了多元数据分析方法,并探讨了算法性能评价的组织结构和软件支撑环境的保障问题。
建立了算法系统性能的全流程评价模型,评价了湍流退化条件下作为小目标检测预处理操作的气动光学效应图像校正算法的性能。分析了图像校正算法对红外小目标检测的影响,探讨了性能评价研究涉及的两个基本问题a)如何评估整个系统性能;b)如何考察在数据变化情况下,比较已存在系统的能力和局限性。试验结果验证了提出的评估模型和方法的有效性。
发展了基于机理分析的性能评价方法和基于科学试验设计的性能评估技术,验证了机理分析性能评价方法对发展新算法的积极作用。提出了一种新的红外弱小目标图像模型——一阶邻域空间分布模型,创造性地将逆/反问题求解的优化问题引入到小目标检测领域,考察了传统背景预测滤波方法中存在的不适问题,由此提出了基于正则化理论的滤波框架,推导出一种新的基于“杂波抑制-目标增强”正则化滤波的小目标检测快速算法。理论分析和试验结果表明新算法具有良好的红外弱小目标检测能力,运算量小、结构简单、利于硬件实时实现;试验结果也验证了评价方法的有效性。
为了降低小目标检测识别评价的复杂性,提高算法研究和评估的效率,提出了支撑算法研究与评价的一体化环境的概念模型。并根据目标识别算法域和软件编程域的专家知识,为支撑环境提出了一种新的基于知识的计算模型——专家系统模型,论述了它的计算机表达方法和实现技术。实践表明,基于该模型的开发可大大加快新算法原型的建模,简化了开发过程,提高了开发效率,使开发和管理更简易、有效,从而能够提升算法与系统研制的生产力。
The detection and recognition of small target is an important component and research direction of automatic target recognition. The problem of detection and tracking of dim target comes from application of long-range surveillance system. In recent years, researchers pay more and more attention on the detection work of dim target in infrared (IR) image sequence. It is an increasingly impending requirement to evaluate the existing or developing algorithms and systems of small target detection and recognition, and to study and develop new algorithms for small target detection by way of performance evaluation. So the performance evaluation is crucial to study and develop new algorithms and systems of detecting and recognizing small targets, to look for the flexibility boundary and to improve the system’s performance.
The purpose of the paper is to present new theories, methods and supporting environment models of performance evaluation for small target detection algorithms and their systems. The work of the paper is to provide theory foundation and experience support for developing the algorithms and systems of the National Natural Science Foundation of PR China and the National defense key advanced research project of PR China. The work of the paper focuses on three aspects. First of them is the research work of the basic performance evaluation framework of algorithm systems of target detection and recongintion. The second is the research work of new performance evaluation methods for small target detection and recognition, including mechanism analysis method, performance evaluation based on experimental design methodology, performance estimation technology of the full processing flowchart, which are used to estimate the existion algorithms or improve and develop new algorithms. The last is the research work of the theoretic models of supporting algorithm development and performance evaluation of small target detection and recognition, and its implementation technologies are also discussed. The work of the paper provides technologic support of research and development of small target detection, recognition.
By analyzing the evolution of performance evaluation of target recognition algorithm, the viewpoint is proposed that the process of the algorithm development and the process of performance estimation should be unified into one framework. The theories and methods of performance evaluation are discussed, the system identification model is introduced into the performance modeling of algorithm systems, and the multivariate data analysis approaches are developed. The organization and software supporting environment for the performance evaluation are discussed.
The performance estimation model of the full processing flowchart for analyzing algorithm systems is made, which is applied to analyze the effects of image restoration on small target acquisition from the turbulence-degraded images is presented. The effect of the restoration operation on infared point target detection is analyzed. The broad basic problems during performance evaluation research are addressed: a) how to investigate the system as a whole, and b) how to evaluate the performance on realistic and varied data to determine and compare both the capabilities and limits of the existing systems. The evaluation experimental results demonstrate the validity of the proposed model and methods.
The new mechanism analysis method and the evaluation technology based on scientific experimental design are presented, and the active effect of the mechanism analysis method on developing novel algorithms is demonstrated. A new model of IR dim small target image– a 1-rank adjacent space distribution model is made. The optimization problem of inverse problem is applied to the domain of small target detection, and the ill-pose problem of conventional clutter background prediction methods is analyzed. Based on this, a filtering framework using regularization technology is presented and a novel fast filtering method with‘clutter-removal target-preserving’regularization is proposed. Detailed theoretical analyses and experimental results show that this new method provides good filtering results and robust adaptability of small IR target detection, moreover, its little computing complexity and simple computing structure are suitable to be implemented in real-time system.
In order to reduce the complexity of evaluating small target detection and recognition, and to enhance the efficiency of algorithm development and estimation, a new supporting environment concept model for algorithm development and performance evaluation is presented. Based on expert knowledge in software programming domain and automatic target recognition domain, a new knowledge-based computing model using expert system is proposed, and its representation method and implementation technologies of the expert system are discussed. The practice shows that the development based on this new model can improve modeling of a novel algorithm prototype and reduce the developing process to get a higher efficiency; and it aso make the development and management more easier and efficient, which improve the productivity of algorithms and systems.
本论文工作的目的是结合国家自然科学基金重点项目和国防重点预研课题,研究小目标检测识别算法性能评价的理论、方法与支撑环境模型,为算法与系统的研制提供理论依据和试验支撑。因此,本文拟从三方面展开研究:一是目标检测识别算法性能评价的基本框架研究;二是小目标检测识别算法性能评价新方法研究,包括机理分析方法、基于试验设计方法学的性能评价方法、全流程性能评价技术等,用以评估现有算法或支撑发展新算法;三是小目标检测识别算法研究与性能评价支撑环境理论模型及其实现技术的研究。为小目标检测识别技术研究一体化提供支持。
本文通过对目标检测识别算法性能评估现状的分析,提出了将算法研究与性能评估过程统一到一个框架的观点。论述了性能评估的基本原理和方法,把系统辨识模型引入到算法系统性能的建模中,发展了多元数据分析方法,并探讨了算法性能评价的组织结构和软件支撑环境的保障问题。
建立了算法系统性能的全流程评价模型,评价了湍流退化条件下作为小目标检测预处理操作的气动光学效应图像校正算法的性能。分析了图像校正算法对红外小目标检测的影响,探讨了性能评价研究涉及的两个基本问题a)如何评估整个系统性能;b)如何考察在数据变化情况下,比较已存在系统的能力和局限性。试验结果验证了提出的评估模型和方法的有效性。
发展了基于机理分析的性能评价方法和基于科学试验设计的性能评估技术,验证了机理分析性能评价方法对发展新算法的积极作用。提出了一种新的红外弱小目标图像模型——一阶邻域空间分布模型,创造性地将逆/反问题求解的优化问题引入到小目标检测领域,考察了传统背景预测滤波方法中存在的不适问题,由此提出了基于正则化理论的滤波框架,推导出一种新的基于“杂波抑制-目标增强”正则化滤波的小目标检测快速算法。理论分析和试验结果表明新算法具有良好的红外弱小目标检测能力,运算量小、结构简单、利于硬件实时实现;试验结果也验证了评价方法的有效性。
为了降低小目标检测识别评价的复杂性,提高算法研究和评估的效率,提出了支撑算法研究与评价的一体化环境的概念模型。并根据目标识别算法域和软件编程域的专家知识,为支撑环境提出了一种新的基于知识的计算模型——专家系统模型,论述了它的计算机表达方法和实现技术。实践表明,基于该模型的开发可大大加快新算法原型的建模,简化了开发过程,提高了开发效率,使开发和管理更简易、有效,从而能够提升算法与系统研制的生产力。
The detection and recognition of small target is an important component and research direction of automatic target recognition. The problem of detection and tracking of dim target comes from application of long-range surveillance system. In recent years, researchers pay more and more attention on the detection work of dim target in infrared (IR) image sequence. It is an increasingly impending requirement to evaluate the existing or developing algorithms and systems of small target detection and recognition, and to study and develop new algorithms for small target detection by way of performance evaluation. So the performance evaluation is crucial to study and develop new algorithms and systems of detecting and recognizing small targets, to look for the flexibility boundary and to improve the system’s performance.
The purpose of the paper is to present new theories, methods and supporting environment models of performance evaluation for small target detection algorithms and their systems. The work of the paper is to provide theory foundation and experience support for developing the algorithms and systems of the National Natural Science Foundation of PR China and the National defense key advanced research project of PR China. The work of the paper focuses on three aspects. First of them is the research work of the basic performance evaluation framework of algorithm systems of target detection and recongintion. The second is the research work of new performance evaluation methods for small target detection and recognition, including mechanism analysis method, performance evaluation based on experimental design methodology, performance estimation technology of the full processing flowchart, which are used to estimate the existion algorithms or improve and develop new algorithms. The last is the research work of the theoretic models of supporting algorithm development and performance evaluation of small target detection and recognition, and its implementation technologies are also discussed. The work of the paper provides technologic support of research and development of small target detection, recognition.
By analyzing the evolution of performance evaluation of target recognition algorithm, the viewpoint is proposed that the process of the algorithm development and the process of performance estimation should be unified into one framework. The theories and methods of performance evaluation are discussed, the system identification model is introduced into the performance modeling of algorithm systems, and the multivariate data analysis approaches are developed. The organization and software supporting environment for the performance evaluation are discussed.
The performance estimation model of the full processing flowchart for analyzing algorithm systems is made, which is applied to analyze the effects of image restoration on small target acquisition from the turbulence-degraded images is presented. The effect of the restoration operation on infared point target detection is analyzed. The broad basic problems during performance evaluation research are addressed: a) how to investigate the system as a whole, and b) how to evaluate the performance on realistic and varied data to determine and compare both the capabilities and limits of the existing systems. The evaluation experimental results demonstrate the validity of the proposed model and methods.
The new mechanism analysis method and the evaluation technology based on scientific experimental design are presented, and the active effect of the mechanism analysis method on developing novel algorithms is demonstrated. A new model of IR dim small target image– a 1-rank adjacent space distribution model is made. The optimization problem of inverse problem is applied to the domain of small target detection, and the ill-pose problem of conventional clutter background prediction methods is analyzed. Based on this, a filtering framework using regularization technology is presented and a novel fast filtering method with‘clutter-removal target-preserving’regularization is proposed. Detailed theoretical analyses and experimental results show that this new method provides good filtering results and robust adaptability of small IR target detection, moreover, its little computing complexity and simple computing structure are suitable to be implemented in real-time system.
In order to reduce the complexity of evaluating small target detection and recognition, and to enhance the efficiency of algorithm development and estimation, a new supporting environment concept model for algorithm development and performance evaluation is presented. Based on expert knowledge in software programming domain and automatic target recognition domain, a new knowledge-based computing model using expert system is proposed, and its representation method and implementation technologies of the expert system are discussed. The practice shows that the development based on this new model can improve modeling of a novel algorithm prototype and reduce the developing process to get a higher efficiency; and it aso make the development and management more easier and efficient, which improve the productivity of algorithms and systems.
引文
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