Traditional navigation relies on visual alignment with leading lights, a task typically monitored by bridge officers over extended periods. This process can lead to fatigue-related human factor errors, increasing the risk of maritime accidents and environmental damage. To address this issue, this study explores the use of convolutional neural networks (CNNs), evaluating different training strategies and hyperparameter configurations to assist officers in identifying deviations from proper visual leading. Using video data captured from a navigation simulator, we trained a lightweight CNN capable of advising bridge personnel with an accuracy of 86% during night-time operations. Notably, the model demonstrated robustness against visual interference from other light sources, such as lighthouses or coastal lights. The primary source of classification error was linked to images with low bow deviation, largely influenced by human mislabeling during dataset preparation. Future work will focus on refining the classification scheme to enhance model performance. We (1) propose a lightweight CNN based on SqueezeNet for night-time ship navigation, (2) expand the traditional binary risk classification into six operational categories, and (3) demonstrate improved performance over human judgment in visually ambiguous conditions.

In the maritime industry, it is common for crew members to be unfamiliar with their workplace until they are on board. This limits evacuation time, which is critical in emergency situations such as fire or shipwreck. The aim of this work is therefore to increase the safety of new crew members by showing them the location of escape routes before they board the ship. For this purpose, the use of 360 technology is proposed. A 360 tour is a virtual, interactive experience that allows users to explore a location as if they were physically there. On a ship, this allows the crew to know the escape routes and make quicker and safer decisions on the most appropriate route. This paper shows how a 360° tour developed and tested on a merchant vessel. The evacuation time from the engine room has been measured for a group of people. The results showed that the 360 tour had a very positive impact on the evacuation time for these people who had not previously been physically present in the area.