Autonomous Detection and Approach Tracking of Moving Ship on the Sea by VTOL UAV based on Deep Learning Technique through Simulated Real-time On-Air Image Acquisitions

Abstract

Real-time detection and tracking moving ships as well as locating the helipad on that ship is still a challenge in critical missions at sea. By using VTOL (Vertical Take-Off and Landing) UAV (Unmanned Aerial Vehicle) types, it allows both the ability to fly approaching the ship and also be able to land vertically on the helipad. This paper proposes a SITL (Software In The Loop) system to verify the automatic detection and tracking of ships moving at sea for the VTOL UAV during the mission and an algorithm to guide VTOL UAV mode selection in the process of approaching and landing on ships moving at sea. On-air images collected from the VTOL UAV’s camera in the X-Plane 11 simulation environment are used to train Deep Learning computer vision algorithms. Real-time ship detection algorithm with up to 125 FPS and 96% accuracy. From the results of the ship and helipad detection, we propose an algorithm to assist the transition of flight modes of VTOL UAV during the tracking and landing mission on a moving ship at sea.