Detection and classification of galaxy morphology based on YOLOv5

Detection and morphological classification of galaxies are important steps to study the formation, structure and evolution of galaxies. However, with the development of large-scale sky surveys such as dark Energy Spectral Survey (DESI), the image data of galaxies are increasing rapidly, and the problem of low classification efficiency caused by excessive image data is difficult to be solved by traditional classification methods. Machine learning algorithms can help astronomers efficiently and automatically detect and classify different galaxies on large astronomical datasets. Therefore, combining with the current popular YOLO visual detection model, this paper proposes a galaxy shape detection model based on YOLOv5.In this paper, affine transformation is firstly carried out on the image, then bilateral filtering and sharpening are used to enhance the data, and for small target detection, the method of reducing the convolution step is adopted. Finally, the YOLOv5 model was used to detect and classify spiral galaxies, elliptical galaxies, lensed galaxies (bar galaxies) and stars in the galaxy images. The mAP@0.5 of the model reached 87.63%, which could accurately detect the positions of different galaxies and classify them.