An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris

Sun, Quan; Niu, Zhaodong; Li, Haijing; Luo, Li; Lin, Xiaotian

doi:10.3390/s19184026

Cited by 10 publications

(6 citation statements)

References 37 publications

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“…, y, k) represents the target gray value of the image. B(x, y, k) represents the background gray value of the image, including noise and uneven sky background (Sun et al 2019;Castronuovo 2011). Figure 3 shows the overall structure of the algorithm.…”

Section: Image Processing Channelmentioning

confidence: 99%

Faint Space Debris Detection Algorithm Based on Small Aperture Telescope Detection System

Jiang¹,

Liu²,

Kang³

et al. 2022

Res. Astron. Astrophys.

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Ground-based photoelectric observation has unique advantages in space target observation. However, due to the weak light-gathering ability of small-aperture optoelectronic observation telescopes, the space debris in the image is weak and easily drowned in noise. In order to solve the above problems, we use digital image processing technology to extract faint space debris. We propose a high detection rate space debris automatic extraction algorithm, aiming to automatically detect space debris. We first establish a new space target description model. It is mainly divided into two stages. The purpose of the first stage is to reduce the influence of a large number of stars. A hybrid filtering of wavelet transform and guided filtering for three consecutive frames, and the reconstructed wavelet is taken the median value can achieve the effect of eliminating stars. In the second stage, we adopt the method of robust principal component analysis and attribute the problem of target detection to the problem of separating the target and background of a single frame of image. After a large number of experimental results analysis, it is proved that the algorithm can effectively detect weak debris in the monitoring system of small aperture telescope, and has high precision and low computational complexity.

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Section: Image Processing Channelmentioning

confidence: 99%

Faint Space Debris Detection Algorithm Based on Small Aperture Telescope Detection System

Jiang¹,

Liu²,

Kang³

et al. 2022

Res. Astron. Astrophys.

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“…Xi et al 2 used feature learning of candidate regions by a trained deep learning network to detect space debris, and one dimensional mean iteration method was proposed to correct the nonuniform background information. Sun et al 3 presented an image adaptive fast registration algorithm and an enhanced dilation difference algorithm to realize an adaptive real-time detection algorithm for Geosynchronous orbit(GSO) debris. Salvatore et al 4 presented an algorithm based on a hybrid image-and event-based using dynamic vision sensing technology to detect space objects in GSO.…”

Section: Introductionmentioning

confidence: 99%

A space debris detection and tracking method for wide-field surveillance

Gao

Wang

Liu

et al. 2023

Ninth Symposium on Novel Photoelectronic Detection Technology and Applications

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Space debris detection is important for space asset protection and space situational awareness. The current environment of man-made satellites and space debris objects in Earth orbits increases rapidly, so does the probability of collision between them. In this paper, we propose a space debris detection method based on image alignment and connected region analysis. First, the median filter and an improved top-hat filter are used for pre-process of the original images, which can eliminate thermal noises and improve optical distribution integrity of targets. Second, a feasible and easy-to-implement connected region labeling method is used for centroid extraction of suspected targets. Meanwhile, several saliency features of targets are used for suspected targets confirmation and false alarms elimination. Then, we use stars in high-brightness magnitude as feature points for accurate interframe registration, which can suppress the influence of platform shaking and background movement on dim target trajectory associations. Finally, data association is used for target trajectory extraction. The experiment is performed using one astronomical image sequence, the results show that the proposed method is robust and efficient on complex backgrounds.

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“…Their method is more complicated and cannot repair possible accidents in real time. 5 High dynamic range (HDR) images are usually obtained by capturing multiple images of a scene under different exposures. Popovic et al designed a new multicamera platform that can construct and render HDR panoramic videos in real time with a resolution and frame rate of 25 fps.…”

Section: Introductionmentioning

confidence: 99%

“…Combining mathematical morphology, threshold segmentation, and global nearest-neighbor multitarget tracking algorithm, the functions of image background suppression, registration, suspicious target extraction, and multitarget tracking are realized. Their method is more complicated and cannot repair possible accidents in real time 5 . High dynamic range (HDR) images are usually obtained by capturing multiple images of a scene under different exposures.…”

Section: Introductionmentioning

confidence: 99%

(Retracted) Real-time detection of panoramic multitargets based on machine vision and deep learning

2022

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. With the continuous development of computer vision technology, moving object detection technology has been paid enough attention and made great progress. Many new methods and new equipment have been developed. As an important part of computer vision, it has important applications in battlefield reconnaissance, video surveillance, image compression and retrieval, human–computer interaction and other research fields, moving object detection and tracking algorithm has always been a research hotspot. We mainly study the panoramic multitarget real-time detection based on machine vision and deep learning. By studying the principle of multitarget real-time detection based on machine vision and deep learning, the panoramic multitarget real-time detection model based on machine vision and deep learning is determined. The principle and correction effect of existing image distortion correction algorithm are analyzed, and the existing problems are summarized. Aiming at the problem that the existing image distortion correction effect is not good, a new method based on machine vision and deep learning is proposed. A real-time panoramic multitarget detection method based on degree learning is proposed. The experimental results show that when the target is moving at medium speed and slow speed, the success rate of tracking is 97% and 95%, respectively; the probability of successful target detection is 100% and 97%, respectively. Experimental results show that the improved method can solve the problem of particle degradation and improve the accuracy of real-time detection.

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An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris

Cited by 10 publications

References 37 publications

Faint Space Debris Detection Algorithm Based on Small Aperture Telescope Detection System

Faint Space Debris Detection Algorithm Based on Small Aperture Telescope Detection System

A space debris detection and tracking method for wide-field surveillance

(Retracted) Real-time detection of panoramic multitargets based on machine vision and deep learning

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