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纸质出版日期： 2020-09-16 ，
录用日期： 2020-01-15
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李俊宏, 张萍, 王晓玮, 黄世泽. 红外弱小目标检测算法综述[J]. 中国图象图形学报, 2020,25(9):1739-1753.
Junhong Li, Ping Zhang, Xiaowei Wang, Shize Huang. Infrared small-target detection algorithms: a survey[J]. Journal of Image and Graphics, 2020,25(9):1739-1753.
红外探测技术具有不受环境等因素干扰的优势，在红外制导、预警等军事领域的应用日益广泛。随着对红外弱小目标检测技术的研究越来越深入，相应的检测方法越来越多样。本文通过对红外弱小目标图像中目标与背景的特性以及红外弱小目标检测技术难点问题进行分析，根据当前是否利用帧间相关信息，分别从基于单帧红外图像和基于红外序列两个角度，选取了相应的红外弱小目标算法进行对比，对其中典型算法的原理、流程以及特点等进行了详细综述，并对每类检测算法的性能进行了比较。针对红外弱小目标图像信噪比低的特点，对红外弱小目标检测算法的难点问题进行分析，给出了目前各种算法的解决方法和不足，探讨红外弱小目标检测算法的发展方向，即研究计算量小、性能优、鲁棒性强、实时性好和便于硬件实现的算法。
Infrared acquisition technologies are not easily disturbed by environmental factors and have strong penetrability. In addition
the effect of infrared acquisition is mainly determined by the temperature of the object itself. Therefore
such technology has been widely used in the military field
such as in infrared guidance
infrared antimissile
and early warning systems. With the rapid development of computer vision and digital image processing technologies
infrared small-target detection has gradually become the focus and challenge of research
and the number of relevant methods and kinds of infrared small-target detection techniques are increasing. However
given the characteristics of small imaging area
long distance
lack of detailed features
weak shape features
and low signal-to-noise ratio
infrared dim- and small-target detection technology has always been a key technical problem in infrared guidance systems. In this study
two kinds of methods
which are based on single-frame images and infrared sequence and extensively used at present
are reviewed. This work serves as basis for follow-up research on the theory and development of small-target detection. The corresponding infrared small-target algorithm is selected for comparison on the basis of the analysis of the characteristics of the target and background in infrared small-target images and the difficulties of infrared small-target detection technology
in accordance with whether the interframe correlation information is used
and from the perspective of single-frame infrared image and infrared sequence. Single-frame based algorithms can be divided into three categories
including filtering methods
human vision system based methods low-rank sparse recovery base methods.The method based on filtering estimates the background of infrared images
using the frequency difference among the target
background and clutter to filter the background and clutter
to achieve the effect of background suppression. The method based on human vision systems mainly uses the visual perception characteristics of human eyes
that is
the appearance of small targets results in considerable changes of local texture rather than global texture. In recent years
the method based on low-rank sparse recovery has been widely used; it is also an algorithm with improved effect in single-frame image detection. This kind of algorithm maximizes the sparsity of small targets
the low rank of backgrounds
and the high frequency of clutter. Moreover
it uses optimization algorithms to solve the objective function and gradually improve the accuracy of detection in the process of iteration. However
this kind of infrared small-target detection method based on single-frame images requires a high signal-to-noise ratio and does not take advantage of the correlation between adjacent frames; thus
it is prone to false detection and demonstrates a relatively poor performance in real time. Therefore
a sequence-based detection method based on spatial-temporal correlation is introduced. For the detection of small moving infrared targets
prior information
such as the shape of small targets
the continuity of gray level change in time
and the continuity of moving track
is key to segment noise and small targets from infrared images effectively. Therefore
in accordance with the order of using these prior information
current mainstream infrared moving small-target detection methods are divided into two categories: detect before motion (DBM) and motion before detect (MBD). These two kinds of algorithms have different application ranges according to their own characteristics. The DBM method is relatively simple
easy to explement
and widely used in tasks with high real-time requirements. By contrast
the MBD method has high detection rate and low false alarm rate and can achieve good detection results in low signals to clutter ratio backgrounds. In this review
the principle
process
and characteristics of typical algorithms are introduced in detail
and the performance of each kind of detection algorithm is compared. At present
infrared small-target detection technologies may have reliable performance in short-term small-target detection and tracking tasks; however
the difficulty of small-target detection is prominent due to complex application scenarios
high requirements for long-term detection
and the particularity of target and background in practical applications. Therefore
according to the characteristics of infrared small targets
this work analyzes the difficulties of infrared small-target detection methods
provides solutions and shortcomings of various algorithms
and discusses the development direction of infrared small-target detection. Thus far
infrared small-target detection technologies have made remarkable progress and have been widely used in infrared guidance and antimissile tasks. However
infrared small-target detection technologies still suffer from some problems. For the characteristics of infrared small-target detection
we need to test and improve the detection theory of small targets further. To improve the detection effect of small targets in infrared images
we must constantly study the corresponding detection methods and improve the schemes. The application of infrared dim- and small-target detection is challenging and complex. The robustness and accuracy of the corresponding algorithms are constantly improved
and the detection speed is also required to meet real-time requirements. Combined with the application characteristics and scope of different military equipment
a universal overseas small-target detection algorithm should be studied. The algorithm should have high accuracy and robustness and must meet real-time requirements to enhance the all-weather reconnaissance capability and the target battlefield information collection capability of the equipment. Therefore
we can also summarize the major development directions of infrared small-target detection technology in the future. First
from the perspective of image fusion of different imaging systems
imaging quality is improved. Second
the existing algorithm is improved by combining the spatial-temporal information of images and the idea of iterative optimization. Third
several datasets are collected
and deep learning methods are explored to improve the accuracy of detection algorithm. Lastly
the improvements of hardware systems are used to accelerate the algorithm and improve the real-time detection. In the future
we will conduct corresponding research from these directions.
红外图像红外序列红外弱小目标低秩稀疏表示小目标检测
infrared imageinfrared sequencesinfrared small targetlow-rank and sparse representation(LRSR)small-target detection
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