Optimized Custom Dataset for Efficient Detection of Underwater Trash
This work addresses the challenge of quantifying submerged marine debris for environmental protection, but it appears incremental as it focuses on dataset creation and application of existing methods.
The paper tackled the problem of detecting submerged underwater trash by developing a custom dataset and an efficient detection approach, aiming to enhance detection accuracy in deep submerged environments using state-of-the-art deep learning architectures.
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.