Detection and tracking for the awareness of surroundings of a ship based on deep learning. Issue 5 (23rd September 2021) - Lee, Won-Jae; Roh, Myung-Il; Lee, Hye-Won; Ha, Jisang; Cho, Yeong-Min; Lee, Sung-Jun; Son, Nam-Sun
Abstract: To prevent maritime accidents, it is crucial to be aware of the surrounding environment near ships. The images recorded by a camera mounted on a ship could be used for the awareness of other ships surrounding it. In this study, ship awareness was performed using three procedures: detection, localization, and tracking. Initially, ship detection was performed using the deep learning-based detection model, YOLO (You Only Look Once) v3, based on the camera image. A virtual image dataset was constructed using Unity to overcome the difficulty of obtaining camera images onboard with various sizes of ships, and to improve the detection performance. This was followed by the localization procedure in which the position of the horizon on the image was calculated using the orientation information from the ship. Subsequently, the position of the detected ship in the spatial coordinate system was calculated using the horizon information. Following this, the position, course over ground, and speed over ground of the target ships were tracked in the time domain using the extended Kalman filter. A deep learning model that determines the heading of the ship in the image was proposed to utilize abundant information of cameras, and it was used to set the initial value of the Kalman filter. Finally, the proposed method for the awareness of ships was validated using an actual video captured from a camera installed on an actual ship with various encountering scenarios. The tracking results were compared with actual automatic identification system data obtained from other ships. As a result, the entire detection, localization, and tracking procedures showed good performance, and it was estimated that the proposed method for the awareness of the surroundings of a ship, based on camera images, could be used in the future. Graphical Abstract: