DSDNet: Target Detection Algorithm for SDSS Photometric Images Based on Convolutional Neural Networks

The Sloan Digital Sky Survey (SDSS), a large-scale astronomical survey project, has released a vast volume of photometric images. These images play a pivotal role in deriving fundamental parameters of celestial objects and investigating the structure of the universe. Nevertheless, in dense star fields, the characteristics of celestial sources are intricate, rendering traditional methods incapable of conducting precise analyses. To address this challenge, this paper introduces a new algorithm named DSDNet for detecting celestial sources in dense star fields and counting them. During the feature extraction phase, DSDNet generates larger feature maps, thereby preserving more information about small targets. By incorporating a convolutional attention module, the model's capacity to learn the features of celestial sources in dense star fields is augmented. Furthermore, to more effectively manage the blending phenomenon among sources, DSDNet integrates CNN and Transformer architectures, enhancing the model's ability to comprehend global features. Experimental findings show that DSDNet exhibits excellent performance, attaining an F1-score of 97.23%. This makes it a valuable resource for aiding SDSS in processing dense star field images.