Current Issue Cover

李俊宏, 张萍, 王晓玮, 黄世泽(电子科技大学光电科学与工程学院, 成都 610054)

摘 要
Infrared small-target detection algorithms: a survey

Li Junhong, Zhang Ping, Wang Xiaowei, Huang Shize(School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China)

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.