Spatial Multi-object Recognition Based on Deep Learning

摘要

With the rapid development of spacecraft technology, spacecraft, which is mainly represented by satellites, has become an important military resource for the extraordinary success of space attack and defense in various countries. Accurately identifying the type of satellite and the components of the satellite's windsurfing, nozzles, and star sensors is important prerequisites and safeguards for space attack and on-orbit maintenance. In this paper, the deep learning based convolutional neural network YOLO model is used to identify the space satellite and its components, and the three dimensional models and the physical models image set of the two satellite models are trained for close-range front view, long distance, occlusion, and motion blur. Satellites and satellite components are identified under different conditions. In some cases , the recognition accuracy of satellite and satellite components is more than 90%, it is of great significance in the field of on-orbit services, space attack and defense confrontation.