Before conducting star point recognition by through star sensors, it is usually necessary to obtain star point position information in advance, which is called star point detection. According to the characteristics of star maps, the main tasks of star point detection include:
Star point detection is a processing method that achieves threshold segmentation of targets and backgrounds in star maps. The specific steps of star detection include clustering, segmentation, labeling, and final centroid positioning of star regions. Star detection is a crucial step before star recognition, and its accuracy plays a crucial role in the successful recognition of subsequent stars. It also determines the accuracy of recognition and the effectiveness of attitude. Therefore, a fast and effective star detection method is crucial. The clustering and segmentation of star points are generally carried out using two methods, one is the global threshold method, and the other is the local threshold method, both of which can be collectively referred to as threshold segmentation methods. When using a star sensor to aim at the sky background for shooting, it can be observed that there is a significant difference in the grayscale of the stars and the background. In this case, empirical thresholds can be used to effectively separate the stars and the background.
However, when encountering the influence of stray light, the background in the star map is very bright due to the presence of stray light, even approaching the brightness values of some weak stars, or directly flooding the star points. Therefore, using a single global threshold segmentation method is not very suitable, and it is easy to overlook one aspect and miss the other, as it cannot ensure that the star points in each region are accurately detected. Therefore, it is necessary to use a local threshold segmentation method. Among them, the commonly used methods include background prediction algorithms. However, the local adaptive threshold method is limited by the complexity of computation, often resulting in slow computation speed and difficult implementation. Moreover, a simple local threshold method cannot be applicable to various working conditions and backgrounds.
At present, most detection algorithms applied on star sensor platforms do not have ideal suppression effects on stray light. Considering that the proportion of star points on the detector target surface is usually very small, about 2 × 2 to 7 × With a size of 7 pixels, star points can also be considered as small targets, and the idea of infrared weak target detection can be used to achieve star point detection. Small object detection is usually divided into two types: based on single frame images and based on video sequences. The first is a detection method based on filtering. It is also a common method used for small object detection in the early days. Filter based detection methods have the characteristic of simple operation and achieve background suppression by designing specific filters.
In addition, combining the idea of frequency domain operation, relevant filters are designed in the frequency domain to suppress noise by utilizing the difference in frequency functions between the target and other backgrounds and noise. Overall, although algorithms have low computational complexity, their detection performance is usually average and can only be applied to background suppression in certain scenarios, with low robustness.
Filter based detection algorithms are mainly divided into the following two types:
Early infrared dim small target detection mostly used spatial filtering algorithms, which designed filtering windows to calculate the differences between the target and background, combined with the differences in grayscale between the target and background to achieve small target detection, and also achieved background suppression; The earliest use was a simple spatial high pass filter, where the grayscale value of weak and small targets is generally higher than the grayscale value of the background. This difference is then used to achieve the final target detection. However, in this method, those isolated noise points are also easily detected, resulting in low detection rate. Later, other scholars proposed new spatial filtering algorithms, defined new open operations, and obtained a new Top-Hat transform, which can solve the problem of traditional Top-Hat transform being unable to distinguish real targets when used for infrared weak target detection.
In addition to spatial filtering methods, effective filters can also be developed in the spectral transform domain to separate the target from background clutter by utilizing the frequency differences between the target, background, and clutter. Common filters for frequency domain filtering include ideal high pass filters, Gaussian high pass filters, and Butterworth filters, all of which exhibit the phenomenon of “ringing” and poor filtering performance. Later, researchers carried out some improvement work on frequency domain filtering, such as using wavelet transform methods and shear wave transform. In addition, there are methods to achieve target detection by changing the corresponding coefficients through singular values [98]. However, due to parameter setting requirements, it cannot adapt to various different scenarios. Overall, compared to algorithms based on spatial filtering, algorithms based on frequency domain filtering have higher complexity, but also have higher detection levels.
The Human Visual System (HVS) mechanism is widely used in object detection, object recognition, and behavior understanding. This type of algorithm utilizes the visual perception characteristics of the human eye and adopts local difference and mutation methods to achieve small object detection tasks. The small target detection methods based on HVS can be specifically divided into the following two types:
Small target detection is achieved by retaining the normative characteristics of the target while suppressing other characteristics. This algorithm has good detection performance for infrared dim small targets that do not require prior knowledge and do not have significant texture features, with low algorithm complexity, but poor ability to suppress background clutter. Some experts have gradually improved the spectral residual algorithm, such as improving the threshold of variance and mean in the amplitude spectrum. The technology of combining corner detection and local feature fusion can effectively achieve object detection and improve the accuracy of detection.
Considering that there is a gradient difference in the grayscale values between neighboring images and weak targets in infrared weak target images, which has significant characteristics and conforms to the characteristics observed by the human visual system, selecting local features can better represent small targets and facilitate detection. However, small object detection algorithms have some limitations in practical use and do not have complete robustness to different complex scenarios.
In summary, filter based methods are mainly suitable for single, uniform continuous backgrounds and scenes with smaller target sizes; The HVS based detection method is mainly suitable for scenes with high target brightness and significant differences from the surrounding background. Similar to star sensor star point detection methods, these algorithms have varying adaptability to targets and environments, and cannot perfectly cope with various types of stray light encountered by star sensors.
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