STAR-YOLO: A Model for Detection of Complex Galaxy Morphology

Abstract Galaxy morphology detection is a pivotal task for unraveling cosmic evolutionary mechanisms, yet existing models exhibit insufficient detection accuracy for irregular and small-target galaxies. To address this, this paper proposes the STAR-YOLO galaxy morphology detection model. The backbone network incorporates the novel Multi-scale Attentive Context Aggregation module, which deeply integrates multi-scale dilated convolution with a progressive spatial-channel attention mechanism to enhance feature extraction for irregular and small galaxies. Meanwhile, we design the lightweight Lightweight Efficient Attention Network module that reduces parameters through channel compression. The proposed Adaptive Focal Spatial-IoU loss function further improves detection performance for small galaxies through dynamic focal mechanisms and scale-invariant optimization. Evaluated on Galaxy Zoo 2 data set, our STAR-YOLO achieves 96.3% mean average precision—a 2.5% improvement over baseline models, with irregular galaxy recognition accuracy notably increasing by 9.3%. Comparative experiments demonstrate superior detection capabilities for multi-target irregular galaxies compared to state-of-the-art models, providing an innovative solution for astronomical image analysis.