Accurately quantifying and removing submerged underwater waste plays a crucial role in safeguarding marine life and preserving the environment. While detecting floating and surface debris is relatively straightforward, quantifying submerged waste presents significant challenges due to factors like light refraction, absorption, suspended particles, and color distortion. This paper addresses these challenges by proposing the development of a custom dataset and an efficient detection approach for submerged marine debris. The dataset encompasses diverse underwater environments and incorporates annotations for precise labeling of debris instances. Ultimately, the primary objective of this custom dataset is to enhance the diversity of litter instances and improve their detection accuracy in deep submerged environments by leveraging state-of-the-art deep learning architectures.

翻译：准确量化并清除水下废弃物对于保护海洋生物和维持生态环境至关重要。尽管检测水面和表层垃圾相对简单，但受光折射、光吸收、悬浮颗粒以及色彩失真等因素影响，量化水下废弃物面临重大挑战。本文通过提出构建一个定制化数据集以及一种针对水下废弃物高效检测的方法来应对这些挑战。该数据集涵盖多样化水下环境，并包含精确标注废弃物实例的注释。最终，该定制数据集的主要目标是通过利用最先进的深度学习架构，增强垃圾实例的多样性，并提升其在深水环境中的检测精度。