This study addresses marine pollution caused by debris entering the ocean through rivers. A physical and bubble barrier system has been developed to collect debris, but an effective identification and classification system for incoming vessels is needed. This study evaluates the effectiveness of deep learning models in identifying and classifying vessels in real time. The YOLO (You Only Look Once) v5 and v8 models are evaluated for vessel detection and classification. A dataset of 624 images representing 13 different types of vessels was created to train the models. The YOLOv8, featuring a new backbone network, outperformed the YOLOv5 model, achieving a high mean average precision (mAP@50) of 98.9% and an F1 score of 91.6%. However, YOLOv8’s GPU consumption increased by 116% compared to YOLOv5. The advantage of the proposed method is evident in the precision–confidence curve (PCC), where the accuracy peaks at 1.00 and 0.937 confidence, and in the achieved frames per second (fps) value of 84.7. These findings have significant implications for the development and deployment of real-time marine pollution control technologies. This study demonstrates that YOLOv8, with its advanced backbone network, significantly improves vessel detection and classification performance over YOLOv5, albeit with higher GPU consumption. The high accuracy and efficiency of YOLOv8 make it a promising candidate for integration into marine pollution control systems, enabling real-time identification and monitoring of vessels. This advancement is crucial for enhancing the effectiveness of debris collection systems and mitigating marine pollution, highlighting the potential for deep learning models to contribute to environmental preservation efforts.

In this paper, the application of machine learning and deep learning algorithms for fault and failure detection in maritime systems is examined, specifically focusing on the detection of pipe ruptures in a ship’s saltwater firefighting (FiFi) system using pressure sensor data. Neural network models were developed to distinguish between normal operational states and anomalies, as well as to accurately locate pipe faults within the ship. Data were collected using real-world tests with FiFi system sensors, capturing both normal operations and simulated pipe ruptures, and were meticulously labeled to facilitate neural network training. Two neural network models were introduced: one for classifying system states (normal or anomalous) based on time-series pressure data, and another for identifying the location of anomalies related to pipe ruptures. Experimental results demonstrated the efficacy of these models in detecting and localizing pipe faults, with performance evaluated using mean squared error (MSE) across different network configurations. The successful implementation of these machine learning and deep learning algorithms highlights their potential for enhancing maritime safety and operational efficiency.

This paper presents the results of the implementation of a virtualized model of the behavior of the firefighting (FiFi) system that allows autonomous detection and response of the system to failures. The developed system which is fed by this virtual model is based on Artificial Intellligence (AI) and will be implemented in the ship’s digital twin, providing it with intelligence. For the elaboration of this virtualized model, we have started with data obtained in the tests carried out on board, as well as the results of simulations of the FiFi system. In these on-board tests/simulations, a double characterization of the system has been carried out: obtaining, on the one hand, data that represents normal operation, and on the other, the rupture and the collapse (total or partial) of the pipes. The combination of both has allowed to obtain a sufficiently broad and representative data collection to carry out the training of an AI - based system.

The range of possibilities offered by the IoT in sectors such as shipbuilding, still unexplored, is very interesting. In a work environment where the flow of information is complicated and information is disaggregated at different points, the aggregation of information and connectivity between systems, jobs, suppliers, etc., will allow improving the efficiency inside a shipyard. In this paper, the challenges of the IIOT in the shipyard that have been addressed in the work carried out by the Research Unit of the University of A Coruna—Navantia are presented. Thus, a process to study the applicability of IoT platforms to ship manufacturing has been started: to test different IoT platforms and find the one that best suits a particular use case. The case chosen is the Preoufitting Workshop.