Enhancing the function of the aids to navigation by practical usage of the deep learning algorithm

• Published in: Journal of navigation Vol. 77; no. 3; pp. 347 - 358
• Main Authors: Sim, Yoontae, Chae, Chong-Ju
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.05.2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; Coastal waters; Collision avoidance; Collisions; Datasets; Deep learning; Information systems; Inland waters; Machine learning; Marine accidents; Maritime industry; Marking; Nautical charts; Navigation; Navigation systems; Navigational aids; Navigational hazards; Neural networks; Passageways; Python; Satellites; Sensors; Shallow water; Wrecks; Singapore; deep learning algorithm; object detection; AtoN detection; artificial intelligence
• ISSN: 0373-4633; 1469-7785
• DOI: 10.1017/S0373463324000353

Information is provided to navigators through advanced onboard navigation equipment, such as the electronic chart display and information system (ECDIS), radar and the automatic identification system (AIS). However, maritime accidents still occur, especially in coastal and inland water where many navigational dangers exist. The recent artificial intelligence (AI) technology is actively applied in navigation fields, such as collision avoidance and ship detection. However, utilising the aids to navigation (AtoN) system requires more engagement and further exploration. The AtoN system provides critical navigation information by marking the navigation hazards, such as shallow water areas and wrecks, and visually marking narrow passageways. The prime function of the AtoN can be enhanced by applying AI technology, particularly deep learning technology. With the help of this technology, an algorithm could be constructed to detect AtoN in coastal and inland waters and utilise the detected AtoN to create a safety function to supplement watchkeepers using recent navigation equipment.