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Modern Floating Marine Macrolitter Monitoring Approaches and Methods and Integration of Machine Learning Techniques
Summary
This review examined modern methods and approaches for detecting floating marine macrolitter, including visual observations, trawling, radar remote sensing, and deep learning techniques. The authors analyzed recent advances in artificial neural networks and machine learning that have significantly improved the accuracy of identifying and classifying marine litter at the sea surface, with key challenges including object diversity and partial submersion.
Marine litter pollution is currently recognized as global problem at the level of all international organizations and conventions related to the marine protection. This review examines modern methods and approaches for detecting floating marine macrolitter. The task of detecting marine litter on the water surface is complicated by a large variety of objects, various degrees of their degradation, predominantly small size, partial immersion in the subsurface layer, colorlessness, disguising within the water, difficult observation conditions. The main approaches today include visual observations (from ships, aircraft), trawling, and remote sensing, especially using radar systems. In the last decade, deep learning methods have made significant progress, which has allowed error recognition and identification to be brought to a new level due to various modifications of artificial neural networks. In this review, we analyze the main research on the presented topic and significant achievements and prospects for the application of artificial intelligence to improve methods for detecting and classifying marine litter larger than 2.5 cm.