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Maritime transport is the most widely used means of transporting goods, and forecasts indicate that it will continue to grow in the coming years, which is why the IMO is regulating energy efficiency and emissions from maritime transport with exhaustive monitoring. The most widely used measure of energy efficiency during operation on a ship is the Energy Efficiency Operational Indicator (EEOI); however, the difficulty in obtaining references for this indicator, together with the great variability that exists between shipowners when choosing terms such as "cargo transported", among others, means that this operational indicator of energy efficiency in ships does not have the expected results. This work develops a two-phase procedure for the selection of representative EEOI values for ships, taking into account parameters such as ship speed, cargo, mode of operation and the subsequent determination of a suitable control system that allows the continuous and real-time implementation of measures to improve energy and environmental efficiency on the ship. The proposed final procedure is effective in terms of improving energy efficiency and emissions, on top of being simple, easily implementable, working in real time and adapting to navigation circumstances while keeping emissions under control. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]