Danilov Aleksandr, Serdiukova Elizaveta
Department of Geoecology, Saint Petersburg Mining University, Saint Petersburg 199106, Russia.
Sensors (Basel). 2024 Aug 6;24(16):5089. doi: 10.3390/s24165089.
Ocean plastic pollution is one of the global environmental problems of our time. "Rubbish islands" formed in the ocean are increasing every year, damaging the marine ecosystem. In order to effectively address this type of pollution, it is necessary to accurately and quickly identify the sources of plastic entering the ocean, identify where it is accumulating, and track the dynamics of waste movement. To this end, remote sensing methods using satellite imagery and aerial photographs from unmanned aerial vehicles are a reliable source of data. Modern machine learning technologies make it possible to automate the detection of floating plastics. This review presents the main projects and research aimed at solving the "plastic" problem. The main data acquisition techniques and the most effective deep learning algorithms are described, various limitations of working with space images are analyzed, and ways to eliminate such shortcomings are proposed.
海洋塑料污染是我们这个时代的全球环境问题之一。海洋中形成的“垃圾岛”每年都在增加，破坏着海洋生态系统。为了有效应对这类污染，有必要准确、快速地识别进入海洋的塑料来源，确定其积聚位置，并追踪废物移动的动态。为此，利用卫星图像和无人机航空照片的遥感方法是可靠的数据来源。现代机器学习技术使漂浮塑料的检测自动化成为可能。本综述介绍了旨在解决“塑料”问题的主要项目和研究。描述了主要的数据采集技术和最有效的深度学习算法，分析了处理太空图像的各种局限性，并提出了消除这些缺点的方法。
