ABSTRACT Detecting non‐functional satellite components is critical for on‐orbit servicing. Current detection methods struggle with complex image noise, motion blur in space environments, and the limited realism of artificially synthesised sample data. To address these challenges, we propose an enhanced you only look once version 8 (YOLOv8)‐based method. In terms of network architecture, we introduce innovative designs for the backbone and neck components. A novel hybrid attention mechanism replaces the conventional approach, improving the perception and processing of intricate image features and significantly enhancing feature extraction. Additionally, we integrate modules inspired by residual networks into the neck structure, improving training adaptability and ensuring robust information transmission. This design highlights key target features while minimising feature attenuation. We also establish the satellite key element (SAKE) dataset under simulated real space conditions, including image noise and jitter blur. This dataset features components such as satellite bodies and solar panels and uses an encoder–decoder network architecture to refine context information. By merging this with a branch preserving high‐resolution details, we enhance dataset expressiveness. Experiments demonstrate that the enhanced algorithm achieves a mean average precision (mAP) of 78.98% on the SAKE dataset, a 2.57% improvement over the original YOLOv8. The refined model effectively detects critical satellite components, showing superior performance in noisy and blurry scenarios.
Bian et al. (Wed,) studied this question.