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In this work a novel ships dataset is proposed consisting of more than 56kimages of marine vessels collected by means of web-scraping and including 12ship categories. A YOLOv3 single-stage detector based on Keras API is built ontop of this dataset. Current results on four categories (cargo ship, navalship, oil ship and tug ship) show Average Precision up to 96% for Intersectionover Union (IoU) of 0.5 and satisfactory detection performances up to IoU of0.8. A Data Analytics GUI service based on QT framework and Darknet-53 engineis also implemented in order to simplify the deployment process and analysemassive amount of images even for people without Data Science expertise.

Real-Time target detection in maritime scenarios based on YOLOv3 model