基于YOLOv5s的卫星光通信初始捕获方法（特邀）

Abstract

To solve the problem of traditional beacon laser spot detection algorithms being susceptible to complex background interference during the initial capture stage of satellite laser communication. YOLOv5s neural network is used to optimize and improve the initial pointing scene of satellite platforms. Selecting the original loss function with the smoothed intersection over union (SIoU) loss function and replacing the original upsampling structure with a lightweight content aware feature recombination (CARAFE) upsampling structure, adding convolutional block attention module (CBAM) attention mechanism to C3 layer, using SimSPPF to replace the original structure, and adding Coordconv structure that is conducive to perceiving position information. The improved neural network has better accuracy than traditional coarse tracking beacon spot detection algorithms, and can accurately detect the position of the spot in complex backgrounds. It is suitable for beacon spot detection in the initial capture stage and coarse tracking stage. The optimized 收稿日期：2024 -01 -13；修回日期：2024 -02 -04；录用日期：2024 -02 -20；网络首发日期：2024 -02 -25 基金项目：国家自然科学基金(62275033， 61775022)、 重庆市自然科学基金(cstc2021jcyj -msxmX0457)、 国家自然科学基金青年 基金 (62205032)、 吉林省科技发展计划项目(20210201139GX)、 长春理工大学青年基金(XQNJJ -2019 -01) 通信作者： *