Artificial Intelligence Vision-Based Monitoring System for Ship Berthing

Abstract

This paper proposes a novel artificial intelligence vision-based monitoring system (AVMS) for ship berthing. To dock a ship, it is necessary to accurately estimate the relative distance between the quay wall and the ship. However, maneuvering large ships near a port is a complicated and difficult procedure. Thus, tugboats push the ship and dock it at the berth under the supervision of a pilot, who receives distance information from a berthing aid system (BAS). The conventional BAS based on laser distance sensors, which is the most widely used approach, is high-priced and limited by the size of the ship. Additionally, if there is an obstacle between the ship and the berth, the distance cannot be measured, since it obscures the laser signal. To address this problem, we develop an AVMS sensor module composed of a low-priced camera, a differential global positioning system (DGPS) receiver, and an inertial measurement unit (IMU) with an algorithm to estimate the distance between ship and berth. To evaluate the performance of the proposed AVMS, field tests are performed at Ulsan port in Korea, and the results are compared with a conventional BAS. From the field test results, the AVMS provides highly accurate estimates and shows robust performance in poor weather conditions compared to the conventional BAS. The AVMS can measure the distance between ship and berth regardless of the size of the ship, since it has a wide field of view. In addition, it provides the pilot with real-time image information of the ship approaching the berth to obtain safe ship berthing.

Keywords

• Artificial intelligence
• berthing aid system (BAS)
• deep learning
• vision sensor
• ship berthing