Addressing the issue of submerged underwater trash is crucial for
safeguarding aquatic ecosystems and preserving marine life. While identifying
debris present on the surface of water bodies is straightforward, assessing the
underwater submerged waste is a challenge due to the image distortions caused
by factors such as light refraction, absorption, suspended particles, color
shifts, and occlusion. This paper conducts a comprehensive review of
state-of-the-art architectures and on the existing datasets to establish a
baseline for submerged waste and trash detection. The primary goal remains to
establish the benchmark of the object localization techniques to be leveraged
by advanced underwater sensors and autonomous underwater vehicles. The ultimate
objective is to explore the underwater environment, to identify, and remove
underwater debris. The absence of benchmarks (dataset or algorithm) in many
researches emphasizes the need for a more robust algorithmic solution. Through
this research, we aim to give performance comparative analysis of various
underwater trash detection algorithms.

通过综合评估现有数据集和先进技术的体系结构，本文旨在为水下废物和垃圾检测建立基线，从而为先进的水下传感器和自主水下车辆提供目标定位技术的基准，最终实现水下环境的探测、废物识别和清除。

深度学习创新在水下废物检测中的应用：一项深入分析

Deep Learning Innovations for Underwater Waste Detection: An In-Depth  Analysis

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。