Ship detection using optical satellite images is a very important task for the field of maritime security, either in search of lost ships or in maritime control of a commercial or military type. Added to this are the advances in the field of Computer Vision, especially in the use of models based on Artificial Intelligence, which allow the construction of robust and more precise detection systems. However, geographic scenarios, typical of a satellite image, limit the development of this type of system since they require the availability of a large number of images in different scenarios. In this paper, a new approach to Ship Detection is proposed using two new data sets labeled with horizontal bounding boxes (HBB). Likewise, a new labeling tool (DATATOOL) is presented that allows better organization and distribution of data. The new data sets, Peruvian Ship Dataset (PSDS) and Mini Ship Dataset (MSDS), have been generated from optical satellite images obtained from different sources. PSDS is created from 22 satellite images of PERUSAT-1 with 0.7m spatial resolution, giving a total of 1310 images. MSDS has been generated using Google Earth satellite images, generating 2993 images of 900x900 pixels. Ships are found both at sea or inshore. Finally, results of the tests using Deep Learning Algorithms such as YOLT and YOLOv4 are presented, following the approach and the proposed tools. Resource and source code available at https://gitlab.com/williamccondori/datatool