Published Paper Details:

The human visual system effortlessly recognizes and identifies objects within its field of view, showcasing remarkable speed and accuracy in tasks such as object detection and identification. Valuable clues regarding the origin and evolution of the universe are intricately woven into the shapes and formations of galaxies. However, automating the classification of galaxies from their images poses challenges due to the faintness of the galaxy images, interference from bright background stars, and inherent image noise. To address these complexities, we propose a method for the automatic detection and classification of galaxies. This method incorporates a data augmentation procedure, enhancing the robustness of trained models against data variations arising from different instruments and contrast-stretching functions. This innovative approach is a pivotal component of an expanding open-source computer vision repository dedicated to processing and analyzing extensive galactical datasets. The repository integrates high-performance deep learning algorithms, specifically leveraging the You Only Look Once (YOLO) model and other advanced techniques in image processing and computer vision. The underlying model, trained through deep learning methodologies, exemplifies the intersection of cutting-edge technology and the exploration of the cosmos.