Lightweight satellite recognition algorithm based on improved YOLOv5

Target detection technology plays a crucial role in the aerospace field, and recognizing satellites with few samples is of great significance for ensuring national space security. However, the limited computing resources of satellites restrict the application of intelligent algorithms. To overcome this issue, this paper proposes a lightweight improvement of the YOLOv5s detection algorithm with the introduction of an attention mechanism. First, the lightweight module GhostNet is used to modify the backbone network, serving as the primary feature extraction network of the model. The Coordinate Attention EMA module is introduced in the neck network to enhance the feature representation capability for detecting target satellites, thereby improving detection accuracy in complex backgrounds. Simulations based on actual sampled data show that the algorithm's parameter count is reduced by 27.6%, achieving a [email protected]:0.95% of 91.21% and a detection rate of 400 FPS. This effectively enhances the model's multi-scale feature representation and fusion capabilities, improving target detection accuracy.