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The expansion of satellite constellations poses a significant challenge to optical ground-based astronomical observations, as satellite trails degrade observational data and compromise research quality. Addressing these challenges requires developing robust detection methods to enhance data processing pipelines, creating a reliable approach for detecting and analyzing satellite trails that can be easily reproduced and applied by other observatories and data processing groups. Our method, called ASTA (Automated Satellite Tracking for Astronomy), combines deep learning and computer vision techniques for effective satellite trail detection. It employs a U-Net based deep learning network to initially detect trails, followed by a Probabilistic Hough Transform to refine the output. ASTA's U-Net model was trained on a dataset with manually labelled full-field MeerLICHT images prepared using the LABKIT annotation tool, ensuring high-quality and precise annotations. This annotation process was crucial for the model to learn and generalize the characteristics of satellite trails effectively. Furthermore, the user-friendly LABKIT tool facilitated quick and efficient data refinements, streamlining the overall model development process. ASTA's performance was evaluated on a test set of 20,000 image patches, both with and without satellite trails, to rigorously assess its precision and recall. Additionally, ASTA was applied to approximately 200,000 full-field MeerLICHT images, demonstrating its effectiveness in identifying and characterizing satellite trails. The software's results were validated by cross-referencing detected trails with known public satellite catalogs, confirming its reliability and showcasing its ability to uncover previously untracked objects.