Deep learning techniques have been explored within the marine litter problem
for approximately 20 years but the majority of the research has developed
rapidly in the last five years. We provide an in-depth, up to date, summary and
analysis of 28 of the most recent and significant contributions of deep
learning in marine debris. From cross referencing the research paper results,
the YOLO family significantly outperforms all other methods of object detection
but there are many respected contributions to this field that have
categorically agreed that a comprehensive database of underwater debris is not
currently available for machine learning. Using a small dataset curated and
labelled by us, we tested YOLOv5 on a binary classification task and found the
accuracy was low and the rate of false positives was high; highlighting the
importance of a comprehensive database. We conclude this survey with over 40
future research recommendations and open challenges.

20 年来，深度学习技术一直在海洋垃圾问题中得到探索，并且在过去的 5 年中取得了快速发展。该研究提供了 28 项最近和最重要的关于深度学习在海洋垃圾识别中的贡献的全面、最新的总结和分析。交叉引用研究结果表明，YOLO 系列方法在目标检测方面明显优于其他方法，但是有很多受认可的贡献明确表示目前没有可用于机器学习的全面的水下垃圾数据库。我们使用我们自己策划和标记的小型数据集，在二元分类任务上测试了 YOLOv5，发现准确率较低，假阳性率较高，凸显了建立全面数据库的重要性。最后，我们提出了 40 多项未来研究建议和开放性挑战。

深度学习在海洋垃圾追踪和检测中的最新应用：一项调查

State of the art applications of deep learning within tracking and  detecting marine debris: A survey

Marine litter poses significant threats to marine and coastal environments,
with its impacts ever-growing. Remote sensing provides an advantageous
supplement to traditional mitigation techniques, such as local cleaning
operations and trawl net surveys, due to its capabilities for extensive
coverage and frequent observation. In this study, we used Radiative Transfer
Model (RTM) simulated data and data from the Multispectral Instrument (MSI) of
the Sentinel-2 mission in combination with machine learning algorithms. Our aim
was to study the spectral behavior of marine plastic pollution and evaluate the
applicability of RTMs within this research area. The results from the
exploratory analysis and unsupervised classification using the KMeans algorithm
indicate that the spectral behavior of pollutants is influenced by factors such
as the type of polymer and pixel coverage percentage. The findings also reveal
spectral characteristics and trends of association and differentiation among
elements. The applied methodology is strongly dependent on the data, and if
reapplied in new, more diverse, and detailed datasets, it can potentially
generate even better results. These insights can guide future research in
remote sensing applications for detecting marine plastic pollution.

本研究旨在使用光学遥感与机器学习算法，研究海洋塑料污染的光谱行为，并评价辐射传输模型在此研究领域中的适用性。结果表明，污染物的光谱行为受到聚合物类型和像素覆盖百分比等因素的影响。这些发现可以指导未来在遥感应用中检测海洋塑料污染的研究。