Purpose

With the continuous development of aerospace technology, space exploration missions have been increasing year by year, and higher requirements have been placed on the upper level rocket. The purpose of this paper is to improve the ability to identify and detect potential targets for upper level rocket.

Design/methodology/approach

Aiming at the upper-level recognition of space satellites and core components, this paper proposes a deep learning-based spatial multi-target recognition method, which can simultaneously recognize space satellites and core components. First, the implementation framework of spatial multi-target recognition is given. Second, by comparing and analyzing convolutional neural networks, a convolutional neural network model based on YOLOv3 is designed. Finally, seven satellite scale models are constructed based on systems tool kit (STK) and Solidworks. Multi targets, such as nozzle, star sensor, solar,etc., are selected as the recognition objects.

Findings

By labeling, training and testing the image data set, the accuracy of the proposed method for spatial multi-target recognition is 90.17%, which is improved compared with the recognition accuracy and rate based on the YOLOv1 model, thereby effectively verifying the correctness of the proposed method.

Research limitations/implications

This paper only recognizes space multi-targets under ideal simulation conditions, but has not fully considered the space multi-target recognition under the more complex space lighting environment, nutation, precession, roll and other motion laws. In the later period, training and detection can be performed by simulating more realistic space lighting environment images or multi-target images taken by upper-level rocket to further verify the feasibility of multi-target recognition algorithms in complex space environments.

Practical implications

The research in this paper validates that the deep learning-based algorithm to recognize multiple targets in the space environment is feasible in terms of accuracy and rate.

Originality/value

The paper helps to set up an image data set containing six satellite models in STK and one digital satellite model that simulates spatial illumination changes and spins in Solidworks, and use the characteristics of spatial targets (such as rectangles, circles and lines) to provide prior values to the network convolutional layer.

中文翻译：

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基于深度学习的多目标识别

目的

随着航空航天技术的不断发展，太空探索的任务逐年增加，对高空火箭的要求也越来越高。本文的目的是提高识别和检测高空火箭潜在目标的能力。

设计/方法/方法

针对空间卫星及其核心成分的高层识别，提出了一种基于深度学习的空间多目标识别方法，该方法可以同时识别空间卫星及其核心成分。首先，给出了空间多目标识别的实现框架。其次，通过对卷积神经网络的比较和分析，设计了一种基于YOLOv3的卷积神经网络模型。最后，基于系统工具包（STK）和Solidworks构建了七个卫星比例模型。选择多目标，例如喷嘴，恒星传感器，太阳能等作为识别对象。

发现

通过对图像数据集进行标记，训练和测试，提出的空间多目标识别方法的准确率为90.17％，与基于YOLOv1模型的识别准确率和识别率相比有所提高，从而有效地验证了图像的正确性。建议的方法。

研究局限/意义

本文仅在理想模拟条件下识别空间多目标，但没有充分考虑在更复杂的空间光照环境，章动，进动，侧倾和其他运动定律下的空间多目标识别。在后期，可以通过模拟更现实的太空照明环境图像或高层火箭拍摄的多目标图像来进行训练和检测，以进一步验证多目标识别算法在复杂空间环境中的可行性。

实际影响

本文的研究验证了基于深度学习的算法在空间环境中识别多个目标的可行性和准确性。

创意/价值

本文有助于建立一个图像数据集，该图像数据集包含STK中的六个卫星模型和一个模拟Solidworks中空间照度变化和旋转的数字卫星模型，并利用空间目标的特征（例如矩形，圆形和直线）来提供先验值到网络卷积层。