关键词: 特征提取, 轻量化, 非合作目标, YOLOv8, 在轨感知

Abstract: Abstract: Aiming at the scene of on-orbit resource-constrained non-cooperative target feature extracting, a lightweight detection model SatYOLO improved based on YOLOv8 is proposed to reduce computational complexity of extracting visual features. Firstly, the Starlight module is proposed to replace the YOLOv8 backbone reducing the amount of computation and parameters; Secondly, designed CGRF neck replacing original neck to fuse multi-scale feature attention and improve detection performance; Finally, a lightweight ShareLN head is proposed replacing original head to reduce the amount of computation, accelerate the detection speed and ensure accuracy, while layer normalization is introduced to alleviate performance degradation caused by the lightweight design. Experimental simulation results show that compared with original model, SatYOLO reduces the number of parameters by 40.9% and the amount of computation by 50.0% while the accuracy is basically equivalent; The proposed model effectively achieves the lightweight goal of the on-orbit non-cooperative target feature extracting system.

Key words: feature extraction, lightweight, non-cooperative target, YOLOv8, on-orbit perception