  In this work a novel ships dataset is proposed consisting of more than 56k
images of marine vessels collected by means of web-scraping and including 12
ship categories. A YOLOv3 single-stage detector based on Keras API is built on
top of this dataset. Current results on four categories (cargo ship, naval
ship, oil ship and tug ship) show Average Precision up to 96% for Intersection
over Union (IoU) of 0.5 and satisfactory detection performances up to IoU of
0.8. A Data Analytics GUI service based on QT framework and Darknet-53 engine
is also implemented in order to simplify the deployment process and analyse
massive amount of images even for people without Data Science expertise.
