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Accurately quantifying and removing submerged underwater waste plays acrucial role in safeguarding marine life and preserving the environment. Whiledetecting floating and surface debris is relatively straightforward,quantifying submerged waste presents significant challenges due to factors likelight refraction, absorption, suspended particles, and color distortion. Thispaper addresses these challenges by proposing the development of a customdataset and an efficient detection approach for submerged marine debris. Thedataset encompasses diverse underwater environments and incorporatesannotations for precise labeling of debris instances. Ultimately, the primaryobjective of this custom dataset is to enhance the diversity of litterinstances and improve their detection accuracy in deep submerged environmentsby leveraging state-of-the-art deep learning architectures.

Optimized Custom Dataset for Efficient Detection of Underwater Trash