Molecular-Clump Detection Based on an Improved YOLOv5 Joint Density Peak Clustering

Abstract

The detection and analysis of molecular clumps can lead to a better understanding of star formation in the Milky Way. Herein, we present a molecular-clump-detection method based on improved YOLOv5 joint Density Peak Clustering (DPC). The method employs a two-dimensional (2D) detection and three-dimensional (3D) stitching strategy to accomplish the molecular-clump detection. In the first stage, an improved YOLOv5 is used to detect the positions of molecular clumps on the Galactic plane, obtaining their spatial information. In the second stage, the DPC algorithm is used to combine the detection results in the velocity direction. In the end, the clump candidates are positioned in the 3D position-position-velocity (PPV) space. Experiments show that the method can achieve a high recall of 98.41% in simulated data made up of Gaussian clumps added to observational data. The efficiency of the strategy has also been demonstrated in experiments utilizing observational data from the Milky Way Imaging Scroll Painting (MWISP) project.