# Dim Staring Debris Targets Detection Method with Dense Long Trailing Star
## 1 Introduction  
With the continuous development of civil spaceflight and the intensive launch of satellite missions, space debris has become an important factor affecting the safe and reliable execution of current aerospace missions [1]. In the early stage, debris monitoring mainly relied on ground-based radar and astronomical telescopes. However, with the increasing number of debris, limited by observation time and field of view, research on space-based debris monitoring has been gradually turned to in the past decade [2]. Space-based debris monitoring mainly relies on optical cameras to image the debris target, obtain astronomical data similar to a \"star list\", and confirm the target information according to the difference in the motion state between the star background and the target [3]. However, with the increase in fragment targets to small scales such as decimeter level and centimeter level, the number of targets in the image and the complexity of content gradually bring great difficulty to the process of information extraction [4]. For remote and small-scale weak targets, the target-follow camera long exposure mode is often used to achieve the target-background SNR, which is sufficient for detection and recognition [5]. In this mode, the staring target forms a point target on the image, the background starforms a long trailing shape, and the non-gazing target forms shapes with different trailing lengths [6]. In the early stage, space debris target detection mainly relied on long-term observation for orbit determination to judge the target [7]. With the development of image processing technology, there are two main types of image processing-based methods: One is to obtain the mapping relationship between a single image and the celestial sphere according to the match of the camera imaging data and the star list, and then perform matching and eliminating for each star target to finally retain the fragment target information [8]. The other is to conduct registration and difference comparison judgment for the front and back frames of continuous imaging and distinguish target information based on different time domain distributions of stars and debris targets [9]. These two methods have good accuracy in data processing under the background of short trailing stars [10], and can give consideration to both real-time performance and accuracy under high SNR by relying on inter-frame differential calculation [11]. The early algorithm mainly aimed at the point target, combined with multi-frame accumulation, to achieve the multi-frame information of the moving target to form a shape similar to \"dotted line\", and the line detection method was used to calculate the position and trajectory [12]. The detection of orbiting debris targets mainly relies on DSP and FPGA to form a heterogeneous computing platform to achieve real-time image processing [13]. There are also some methods to achieve trajectory by high-precision positioning of the target [14]. In the case that multiple debris targets exist simultaneously in the same field of view, the method based on a traditional tracking algorithm can also be adopted [15]. The recent method partially takes deep learning into consideration and uses a deep convolutional network to realize saliency detection for small fragment targets [16]. In literature [17], a fast grid neural network was used to complete the recognition of debris targets, which greatly improved the calculation speed while ensuring recognition accuracy. According to literature [18], the target has a significantly shorter tail compared with the stellar background, and the network-based detection can be realized through the training and learning of target data. The effective processing of weak targets is still the focus of debris monitoring at present. The method based on topological scanning proposed in literature [19] can detect and process a small number of trailing scenes.