With the development of 6G communication technology, satellite Internet will become an important means of achieving global network coverage and high-speed network transmission. The non-uniform distribution of ground equipment and the high-speed mobile characteristics of non-geostationary orbit (NGSO) satellites have created obstacles to the provision of services from satellites to the ground. Reasonable resource management in the multibeam satellite technology system can effectively guarantee that the service demand of ground users can be responded to in time and the resource utilisation rate of users can be improved as much as possible. In this paper, the multibeam satellite system is sorted out from the overall point of view. We also discuss the current work of Artificial Intelligence (AI) in the multibeam satellite processing process to illustrate the key role played by AI in it. Finally, we discuss potential open issues with the aim of providing some insights into the development of multibeam satellite communication systems and their application in AI techniques.
Infrared pedestrian detection often suffers from two problems, i.e., 1) the weak features of infrared images result in false alarms; 2) the generalization ability of infrared pedestrian detection methods is not satisfactory since the infrared images are similar due to the limited the acquisition method. To solve these problems, we proposed a multi-task infrared pedestrian detection method. Firstly, the domain adaptation is introduced to align the feature of visible light images and infrared images, by which visible light images are used as additional data to improve scene diversity and generalization ability. Secondly, the U-Net segmentation network is used to predict the pedestrian activity area, and the detected objects in nonpedestrian parts are filtered out to reduce the false alarm. The experiment results show that, Compared with EfficientDet, our method improved the average precision (AP) by 1.4% on the XDU-NIR2020 dataset.
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