AI-enhanced real-time monitoring of marine pollution: part 1-A state-of-the-art and scoping review

Abstract

Marine pollution, especially from oil spills and litter, poses significant threats to marine ecosystems, aquaculture and fisheries. The proliferation of pollutants requires advanced monitoring techniques to enhance early detection and mitigation efforts. Artificial Intelligence revolutionizes environmental monitoring by enabling rapid and precise pollution detection using remote sensing and machine learning models. This review synthesizes 53 recent studies on Artificial Intelligence applications in marine pollution detection, focusing on different model architectures, sensing technologies and preprocessing methods. The most deployed models of Random Forest, U-Network, Generative Adversarial Networks, Mask Region-based Convolution Neural Network and You Only Look Once demonstrated high prediction rate for detecting oil spills and marine litter. However, challenges remain, including limited training datasets, inconsistencies in sensor data and real-time monitoring constraints. Future research should improve Artificial Intelligence model generalization, integrate multi-sensor data and enhance real-time processing capabilities to create more efficient and scalable marine pollution detection systems.