Federal Institute of Hydrology, Am Mainzer Tor 1, Koblenz 56068, Germany.
Federal Institute of Hydrology, Am Mainzer Tor 1, Koblenz 56068, Germany.
Water Res. 2022 Aug 15;222:118902. doi: 10.1016/j.watres.2022.118902. Epub 2022 Jul 30.
Detection and identification of macroplastic debris in aquatic environments is crucial to understand and counter the growing emergence and current developments in distribution and deposition of macroplastics. In this context, close-range remote sensing approaches revealing spatial and spectral properties of macroplastics are very beneficial. To date, field surveys and visual census approaches are broadly acknowledged methods to acquire information, but since 2018 techniques based on remote sensing and artificial intelligence are advancing. Despite their proven efficiency, speed and wide applicability, there are still obstacles to overcome, especially when looking at the availability and accessibility of data. Thus, our review summarizes state-of-the-art research about the visual recognition and identification of different sorts of macroplastics. The focus is on both data acquisition techniques and evaluation methods, including Machine Learning and Deep Learning, but resulting products and published data will also be taken into account. Our aim is to provide a critical overview and outlook in a time where this research direction is thriving fast. This study shows that most Machine Learning and Deep Learning approaches are still in an infancy state regarding accuracy and detail when compared to visual monitoring, even though their results look very promising.
检测和识别水生环境中的宏观塑料碎片对于了解和应对宏观塑料的分布和沉积的日益出现和当前发展至关重要。在这种情况下，揭示宏观塑料空间和光谱特性的近程遥感方法非常有益。迄今为止，实地调查和目视普查方法是广泛认可的获取信息的方法，但自 2018 年以来，基于遥感和人工智能的技术正在不断发展。尽管它们已被证明具有高效、快速和广泛适用性，但仍存在需要克服的障碍，特别是在数据的可用性和可访问性方面。因此，我们的综述总结了关于不同类型宏观塑料的视觉识别和鉴定的最新研究。重点是数据采集技术和评估方法，包括机器学习和深度学习，但也会考虑到最终产品和已发布的数据。我们的目的是在这个研究方向迅速发展的时代提供一个批判性的概述和展望。本研究表明，与目视监测相比，大多数机器学习和深度学习方法在准确性和细节方面仍处于起步阶段，尽管它们的结果看起来非常有前途。
