Deploying Neural Networks at Sea: Condition Monitoring of the Ropes on the Amerigo Vespucci

Monitoring the condition of ropes aboard historic ships is crucial for both safety and preservation. This study introduces a portable, low-cost imaging device designed for deployment on the Italian training ship Amerigo Vespucci, enabling autonomous acquisition of high-quality images of onboard ropes. The device, built around a Raspberry Pi 3 and enclosed in a 3D-printed protective case, allows the crew to label the state of ropes using colored markers and capture standardized visual data. From 207 collected recordings, a curated and balanced dataset was created through frame extraction, blur filtering using Laplacian variance, and image preprocessing. This dataset was used to train and evaluate convolutional neural networks (CNNs) for binary classification of rope conditions. Both custom CNN architectures and pre-trained models (MobileNetV2 and EfficientNetB0) were tested. Results show that color images outperform grayscale in all cases, and that EfficientNetB0 achieved the best performance, with 97.74% accuracy and an F1-score of 0.9768. The study also compares model sizes and inference times, confirming the feasibility of real-time deployment on embedded hardware. These findings support the integration of deep learning techniques into field-deployable inspection tools for preventive maintenance in maritime environments.