The automatic detection and characterization of ships in optical remote sensing images is a key challenge for maritime surveillance applications. This paper presents an automated system specifically designed for ship detection in medium-resolution Sentinel-2 images. The proposed approach relies on a deep learning model trained on a dataset comprising over 6000 annotated Sentinel-2 images. It achieves a detection rate of 93%, with an average of 2.1 to 3.9 false alarms per Sentinel-2 image. Besides the detection task, it also addresses the estimation of ship lengths as well as ship headings. It yields a mean error of 15.36m ± 19.57m for ship lengths, and estimates ship headings with an accuracy of 93%. This contribution significanly enhances the performance of ship detection and characterization systems in optical remote sensing imagery.