Abstract
This study uses deep learning to introduce an automated approach for inspecting a ship’s safety plans. We compared Convolutional Neural Network (CNN) models and selected the most suitable for symbol detection. Due to the challenge of obtaining sufficient training data, we proposed a method for generating virtual data based on existing safety plans, addressing overfitting concerns. A prototype program was developed and tested on sample safety plans, showcasing its effectiveness in reviewing safety plans by accurately identifying various symbols.