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Optimized Custom Dataset for Efficient Detection of Underwater Trash
Summary
Researchers investigated microplastic accumulation in the tissues of farmed Pacific oysters at aquaculture sites with varying proximity to urban inputs, finding body burden correlated with site-specific microplastic concentrations. Seasonal depuration experiments showed partial clearance of particles over two weeks in clean seawater, but some particle fraction remained sequestered in tissues.
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.