Detection of Strong Gravitational Lenses with a Lens–Topology Relation and Arc-assisted Annotations

Abstract Strong gravitational lenses serve as powerful probes for cosmology and the mass distribution of galaxies. However, identifying these rare systems is often hindered by inaccurate localization and confusion with morphologically similar nonlenses. Recent deep learning–based object detection methods have enabled efficient strong-lens searches in wide-field imaging surveys and yielded a large number of promising candidates. Nevertheless, standard object detection methods typically treat the entire lens system as a single object, neglecting the fine-grained morphology of lensed arcs and the unique topology of strong-lens systems. To address these limitations, we construct a dedicated dataset of galaxy-scale strong lenses using data from the DESI Legacy Imaging Surveys DR10 and propose an arc-assisted annotation strategy that utilizes lensed arcs as auxiliary training targets. Building on D-FINE, a transformer-based object detector, we introduce GL-D-FINE, which incorporates a lens–topology relation (LTR) module to explicitly model the geometric relations between the lens system and its associated arcs. GL-D-FINE achieves 57.0% AP 50:95 and 95.9% recall. Compared to the D-FINE baseline, our model delivers absolute improvements of 3.5% and 3.4% in AP 50:95 and recall, respectively. Furthermore, ablation studies verify that both the arc-assisted annotations and the LTR module contribute to performance improvements, with the LTR module yielding superior detection capability compared to the arc-assisted baseline alone. Our implementation and annotated dataset are publicly available at https://github.com/qintianjian-lab/GL-D-FINE .