基于非恒星跟踪图像特征提取的快速自动多运动目标检测方法
Rapid Automatic Multiple Moving Objects Detection Method Based on Feature Extraction from Images with Non-sidereal Tracking
AI总结：
本文提出一种结合源特征提取与神经网络的快速多运动目标检测方法，可在非恒星跟踪大视场图像中以94.72%精度和5.02%虚警率快速检测不同运动模式目标。
摘要：
对自然和人造运动目标进行光学观测与监视对人类空间安全具有重要意义。非恒星跟踪能够提高系统对运动目标的极限星等，从而有利于空间监视。然而，非恒星跟踪图像包含复杂背景，以及不同亮度和不同运动模式的目标，这对此类图像中的精确多目标检测构成重大挑战，尤其是在大视场望远镜图像中。为了在更高速度下实现更高的检测精度，我们提出了一种结合源特征提取与神经网络的新型目标检测方法。首先，我们的方法从光学图像中提取目标的质心、形状和流量等特征。然后，基于这些特征进行朴素标注，以区分运动目标与恒星。在对标注数据进行平衡处理后，我们利用这些数据训练一个神经网络，旨在建立点状目标和条纹状目标的分类模型。最终，基于神经网络模型的分类结果，通过轨迹关联检测运动模式与被跟踪目标一致的运动目标，而具有不同运动模式的目标则利用形态学统计方法进行检测。基于新疆天文台南山1米望远镜在目标跟踪模式下获取的空间目标图像进行的验证表明，我们的方法实现了94.72%的检测精度，虚警率仅为5.02%，每帧处理时间为0.66秒。因此，我们的方法能够从非恒星跟踪的大视场图像中快速、准确地检测具有不同运动模式的目标。
英文摘要：
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 (WFOV) 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 labeling based on those features to distinguish moving objects from stars. After balancing the labeled 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-meter telescope at Nanshan, Xinjiang Astronomical Observatory, demonstrates that our method achieves 94.72% detection accuracy with merely 5.02% false alarm rate, and a processing time of 0.66s per frame. Consequently, our method can rapidly and accurately detect objects with different motion modes from wide-field images with non-sidereal tracking.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
