Abstract
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. The source code to replicate the results in this paper can be found at GitHub.
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Walia, J.S., Seemakurthy, K. (2023). Optimized Custom Dataset for Efficient Detection of Underwater Trash. In: Iida, F., Maiolino, P., Abdulali, A., Wang, M. (eds) Towards Autonomous Robotic Systems. TAROS 2023. Lecture Notes in Computer Science(), vol 14136. Springer, Cham. https://doi.org/10.1007/978-3-031-43360-3_24
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