Resumen
As the development of autonomous ships is underway in the maritime industry, the automation of ship spare part management has become an important issue. However, there has been little development of dedicated devices or applications for ships. This study aims to develop a Raspberry Pi-based embedded application that identifies the type and quantity of spare parts using a transfer learning model and image processing algorithm suitable for ship spare part recognition. A newly improved image processing algorithm was used to select a transfer learning model that balances accuracy and training speed through training and validation on a real spare parts dataset, achieving a prediction accuracy of 98.2% and a training time of 158 s. The experimental device utilizing this model used a camera to identify the type and quantity of spare parts on an actual ship. It displayed the spare parts list on a remotely connected computer. The ASSM (Automated Ship Spare-Part Management) device utilizing image processing and transfer learning is a new technology that successfully automates spare part management.