基于注意机制的红外小目标检测与跟踪算法研究
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摘要
作为红外自寻的制导、搜索跟踪和预警领域的一项关键技术,红外弱小目标检测与跟踪成为了红外图像处理领域中一项历史悠久且又充满活力的研究课题。对于实际的武器系统来说,如何充分发挥红外目标检测技术的优势,尽可能提高目标的检测距离,以争取在最有利的时机获取目标的相关信息成为决定现代战争胜负的重要因素。距离越远,目标成像面积就会越小,且其遭受复杂背景杂波影响的可能性就会更大,所以相比于其它红外目标检测与跟踪问题而言,如何实现复杂背景条件下红外弱小目标对象的稳健检测和跟踪就成为了一项更具实际意义和挑战性的研究课题。
本论文主要研究复杂背景下红外运动弱小目标的检测与跟踪。主要在红外弱小目标图像预处理、弱小目标检测及弱小目标跟踪三个方面进行了研究。
针对形态学滤波结果容易受到结构元素的大小和形状的影响问题,提出了一种基于自适应形态学Top-hat滤波器的红外弱小目标背景抑制方法,形态学算子采用基于最优保存策略的小生境遗传算法进行优化,通过采用自适应策略控制交叉和变异算子,提高了收敛速度和优化效果。与传统的算法进行比较,实验结果表明该算法对信噪比较低的复杂背景弱小目标图像,可以尽量保留图像中目标的细节,从而减少背景泄露,提高了目标的信噪比。
分析了传统的先检测后跟踪和先跟踪后检测检测算法,这些方法为了对目标的存在性做出判断,往往需要对图像的所有区域进行验证,但实际上所关心的内容通常仅占图像中很小一部分面积。本文提出了一种基于视觉注意机制的红外弱小目标检测方法,该方法将红外弱小目标图像分为外场景和内场景图像,对于外场景图像通过最小错误概率准则抽取图像的感兴趣区域切片,对于内场景图像采用多特征融合的方法检测真实的弱小目标位置。在保证其他性能的前提下,大幅提高了运算效率。由实验结果可以看出,该算法对于较低信噪比的图像序列能够实时的检测出视场中的多个弱小目标,并且运算量小,便于硬件的并行实现,尤其适合大视场下红外弱小目标实时检测。
针对粒子滤波易受到样本贫化现象影响,使粒子丧失了多样性的问题,提出了一种基于量子遗传算法重采样的粒子滤波算法。通过量子遗传算法改善样本集的多样性,减轻了样本贫化现象的同时提高了运动弱小目标跟踪的准确性。采用实际红外图像对所提出的算法进行了仿真实验,结果表明,用该方法得到的状态估计结果优于扩展卡尔曼滤波算法和传统的粒子滤波算法获得的结果。
针对本文提出的基于视觉注意机制的弱小目标检测算法,给出了一种FPGA结合多DSP的硬件实现方案设计。采用三片DSP作为红外图像核心处理单元,采用多处理器松耦合星形拓扑网络系统结构及模块化设计的思想,结合大规模可编程逻辑器件设计并实现了一种具有很好重构性、实时性与适用性的红外弱小目标检测系统。给出了具体的预处理算法、感兴趣区域提取算法以及多特征融合算法的软件设计及实现步骤,在实际系统中对红外弱小目标检测器的测角精度和实时性方面进行了测试,测试结果表明在测角精度和实时性的性能方面达到了设计指标要求。
综上所述,本论文对红外成像目标检测与跟踪相关技术进行了深入的研究,对提出的几种算法均利用实拍的红外图像进行了试验验证,试验结果表明本文提出的算法获得了很好的检测与跟踪效果。
As key techniques in infrared (IR) homing guidance, target search and tracking, warning and so on, IR small target detection and tracking have been regarded as old-line and attractive research topics in the field of IR image processing. As for the real weapon systems, how to make the best of IR target detection techniques to increase the distance of target detection and to obtain the related information about the invading targets, have become important factors to decide the victory or defeat of modern warfare. The longer the distance of target, the less the imaging area of target and the larger the probabilities of targets influenced by backgrounds and clutter will be. Therefore, comparing with other topics in the field of IR target detection and tracking, how small targets can be robustly detected and tracked under complex backgrounds have become the more realistic and challenging research topics.
Detecting and tracking algorithm of moving dim small targets in IR images with complex background are investigated in this dissertation. The main work can be summarized on image preprocessing, target detection and tracking.
Aim at the problem that different size and shape have a large effect on the result of morphological filtering, a novel method for self adaptive morphological Top-hat operator in background suppressing of small target was presented, and the structural elements of the operator are optimized by advanced Genetic Algorithm (GA), adaptive updating strategy was used to control the GA crossover rate and mutation rate and the niche technique based on the method of maintaining optimum is adopted in the GA training step, which reduces the possibility of premature convergence presence and improves the exploitation capabilities of GA.Comparing with the traditional algorithms, the experimental results show that the proposed algorithm can preserve the detail image to the greatest extent, reduce the influence on the background estimation, improve the signal-to-ratio (SNR) greatly and the detection probability in single frame.
Traditional algorithms such as "detect before track (DBT)" and "track before detect (TBD)" are studied, which need compute all region of the image to judge whether the target exists in the sight field, even though the target occupy a small region. An infrared small target detection algorithm based on visual attention mechanism is proposed in this paper to solve this problem. An infrared small target image is divided into inside and outside scene, to the inside scene, a method based on minimum error probability (MSE) is applied to extract the Region of intrest (ROI); to the outside scene, a method based on multi-feasure fusion is applied to identify the targets. The visual attention-based approach reduces the computation complexity, while the other performance aspects are not traded off. The experimental results indicate that the method can effectively detect multi-targets in low signal noise rate infrared image sequences, especially for the realtime detction in the large sight field.
Based on the algorithm of visual attention mechanism, a high performance infrared small target detection system based on TMS320C6416s is designed and implemented in this dissertation, In this system, the data processing units are three pieces of TMS320C6416, loose couple and strar-like structure is adopted. The system is designed under modular designing idea based on DSP and FPGA. An application example on tracking infrared small target under complex background indicates that this system has good reconfiguration, real-time ability and applicability. The experimental results show that the precision of angle measurement and real-time performance can meet the requirement of design index.
Based on the analysis of the cause of sample impoverishment, quanta genetic algorithm was introduced into the particle filter (QGAPF) to solve the problem. Sample impoverishment was relieved by increasing the diversity of samples set, and the ability of estimation and tracking were ameliorated. Experimental results demonstrate that the proposed algorithm can alleviate the effect of the sample impoverishment phenomenon for the particle filter. It is applied to the real infrared small target tracking and the obtained results are compared with particle filtering (PF) and Extended Kalman Filter (EKF). Experimental results show that QGAPF has advantages in the field of state estimation problem.
In summary, the infared target detection and tracking problems are researched in this paper, and new algorithms have been proposted.
本论文主要研究复杂背景下红外运动弱小目标的检测与跟踪。主要在红外弱小目标图像预处理、弱小目标检测及弱小目标跟踪三个方面进行了研究。
针对形态学滤波结果容易受到结构元素的大小和形状的影响问题,提出了一种基于自适应形态学Top-hat滤波器的红外弱小目标背景抑制方法,形态学算子采用基于最优保存策略的小生境遗传算法进行优化,通过采用自适应策略控制交叉和变异算子,提高了收敛速度和优化效果。与传统的算法进行比较,实验结果表明该算法对信噪比较低的复杂背景弱小目标图像,可以尽量保留图像中目标的细节,从而减少背景泄露,提高了目标的信噪比。
分析了传统的先检测后跟踪和先跟踪后检测检测算法,这些方法为了对目标的存在性做出判断,往往需要对图像的所有区域进行验证,但实际上所关心的内容通常仅占图像中很小一部分面积。本文提出了一种基于视觉注意机制的红外弱小目标检测方法,该方法将红外弱小目标图像分为外场景和内场景图像,对于外场景图像通过最小错误概率准则抽取图像的感兴趣区域切片,对于内场景图像采用多特征融合的方法检测真实的弱小目标位置。在保证其他性能的前提下,大幅提高了运算效率。由实验结果可以看出,该算法对于较低信噪比的图像序列能够实时的检测出视场中的多个弱小目标,并且运算量小,便于硬件的并行实现,尤其适合大视场下红外弱小目标实时检测。
针对粒子滤波易受到样本贫化现象影响,使粒子丧失了多样性的问题,提出了一种基于量子遗传算法重采样的粒子滤波算法。通过量子遗传算法改善样本集的多样性,减轻了样本贫化现象的同时提高了运动弱小目标跟踪的准确性。采用实际红外图像对所提出的算法进行了仿真实验,结果表明,用该方法得到的状态估计结果优于扩展卡尔曼滤波算法和传统的粒子滤波算法获得的结果。
针对本文提出的基于视觉注意机制的弱小目标检测算法,给出了一种FPGA结合多DSP的硬件实现方案设计。采用三片DSP作为红外图像核心处理单元,采用多处理器松耦合星形拓扑网络系统结构及模块化设计的思想,结合大规模可编程逻辑器件设计并实现了一种具有很好重构性、实时性与适用性的红外弱小目标检测系统。给出了具体的预处理算法、感兴趣区域提取算法以及多特征融合算法的软件设计及实现步骤,在实际系统中对红外弱小目标检测器的测角精度和实时性方面进行了测试,测试结果表明在测角精度和实时性的性能方面达到了设计指标要求。
综上所述,本论文对红外成像目标检测与跟踪相关技术进行了深入的研究,对提出的几种算法均利用实拍的红外图像进行了试验验证,试验结果表明本文提出的算法获得了很好的检测与跟踪效果。
As key techniques in infrared (IR) homing guidance, target search and tracking, warning and so on, IR small target detection and tracking have been regarded as old-line and attractive research topics in the field of IR image processing. As for the real weapon systems, how to make the best of IR target detection techniques to increase the distance of target detection and to obtain the related information about the invading targets, have become important factors to decide the victory or defeat of modern warfare. The longer the distance of target, the less the imaging area of target and the larger the probabilities of targets influenced by backgrounds and clutter will be. Therefore, comparing with other topics in the field of IR target detection and tracking, how small targets can be robustly detected and tracked under complex backgrounds have become the more realistic and challenging research topics.
Detecting and tracking algorithm of moving dim small targets in IR images with complex background are investigated in this dissertation. The main work can be summarized on image preprocessing, target detection and tracking.
Aim at the problem that different size and shape have a large effect on the result of morphological filtering, a novel method for self adaptive morphological Top-hat operator in background suppressing of small target was presented, and the structural elements of the operator are optimized by advanced Genetic Algorithm (GA), adaptive updating strategy was used to control the GA crossover rate and mutation rate and the niche technique based on the method of maintaining optimum is adopted in the GA training step, which reduces the possibility of premature convergence presence and improves the exploitation capabilities of GA.Comparing with the traditional algorithms, the experimental results show that the proposed algorithm can preserve the detail image to the greatest extent, reduce the influence on the background estimation, improve the signal-to-ratio (SNR) greatly and the detection probability in single frame.
Traditional algorithms such as "detect before track (DBT)" and "track before detect (TBD)" are studied, which need compute all region of the image to judge whether the target exists in the sight field, even though the target occupy a small region. An infrared small target detection algorithm based on visual attention mechanism is proposed in this paper to solve this problem. An infrared small target image is divided into inside and outside scene, to the inside scene, a method based on minimum error probability (MSE) is applied to extract the Region of intrest (ROI); to the outside scene, a method based on multi-feasure fusion is applied to identify the targets. The visual attention-based approach reduces the computation complexity, while the other performance aspects are not traded off. The experimental results indicate that the method can effectively detect multi-targets in low signal noise rate infrared image sequences, especially for the realtime detction in the large sight field.
Based on the algorithm of visual attention mechanism, a high performance infrared small target detection system based on TMS320C6416s is designed and implemented in this dissertation, In this system, the data processing units are three pieces of TMS320C6416, loose couple and strar-like structure is adopted. The system is designed under modular designing idea based on DSP and FPGA. An application example on tracking infrared small target under complex background indicates that this system has good reconfiguration, real-time ability and applicability. The experimental results show that the precision of angle measurement and real-time performance can meet the requirement of design index.
Based on the analysis of the cause of sample impoverishment, quanta genetic algorithm was introduced into the particle filter (QGAPF) to solve the problem. Sample impoverishment was relieved by increasing the diversity of samples set, and the ability of estimation and tracking were ameliorated. Experimental results demonstrate that the proposed algorithm can alleviate the effect of the sample impoverishment phenomenon for the particle filter. It is applied to the real infrared small target tracking and the obtained results are compared with particle filtering (PF) and Extended Kalman Filter (EKF). Experimental results show that QGAPF has advantages in the field of state estimation problem.
In summary, the infared target detection and tracking problems are researched in this paper, and new algorithms have been proposted.
引文
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