.png){kind=icon}
返回
Rapid automatic multiple moving objects detection method based on feature extraction from images with non-sidereal tracking
.png){kind=icon}
OA
AI
DOI：10.1093/mnras/stae2073.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Optically observing and monitoring moving objects, both natural and artificial, is important to human space security. Non-sidereal tracking can improve the system's limiting magnitude for moving objects, which benefits the surveillance. However, images with non-sidereal tracking include complex background, as well as objects with different brightness and moving mode, posing a significant challenge for accurate multi-object detection in such images, especially in wide field-of-view telescope images. To achieve a higher detection precision in a higher speed, we proposed a novel object detection method, which combines the source feature extraction and the neural network. First, our method extracts object features from optical images such as centroid, shape, and flux. Then, it conducts a naive labelling based on those features to distinguish moving objects from stars. After balancing the labelled data, we employ it to train a neural network aimed at creating a classification model for point-like and streak-like objects. Ultimately, based on the neural network model's classification outcomes, moving objects whose motion modes consistent with the tracked objects are detected via track association, while objects with different motion modes are detected using morphological statistics. The validation, based on the space objects images captured in target tracking mode with the 1-m telescope at Nanshan, Xinjiang Astronomical Observatory, demonstrates that our method achieves 94.72 per cent detection accuracy with merely 5.02 per cent false alarm rate, and a processing time of 0.66 s per frame. Consequently, our method can rapidly and accurately detect objects with different motion modes from wide-field images with non-sidereal tracking.
Keyword:
methods: data analysis
techniques: image processing
planets and satellites: detection
期刊
IF:
4.8
论文数:
7.1W
被引数:
25.0W
机构
引用论文
Preparation of Inactivated Oil-Emulsion Vaccines with Avian Viral or Mycoplasma Antigens禽病毒或支原体抗原灭活油乳剂疫苗的制备
.png){kind=icon}
PREAI
分享收藏Bioreductive deposition of palladium (0) nanoparticles onShewanella oneidensiswith catalytic activity towards reductive dechlorination of polychlorinated biphenyls钯 (0) 纳米颗粒在 Shewanella oneidensis 上的生物还原沉积，对多氯联苯的还原脱氯具有催化活性
PREAI
分享收藏
Discovering faint and high apparent motion rate near-Earth asteroids using a deep learning programOAAI
OAAI分享收藏
查看更多内容