  Addressing the issue of submerged underwater trash is crucial for
safeguarding aquatic ecosystems and preserving marine life. While identifying
debris present on the surface of water bodies is straightforward, assessing the
underwater submerged waste is a challenge due to the image distortions caused
by factors such as light refraction, absorption, suspended particles, color
shifts, and occlusion. This paper conducts a comprehensive review of
state-of-the-art architectures and on the existing datasets to establish a
baseline for submerged waste and trash detection. The primary goal remains to
establish the benchmark of the object localization techniques to be leveraged
by advanced underwater sensors and autonomous underwater vehicles. The ultimate
objective is to explore the underwater environment, to identify, and remove
underwater debris. The absence of benchmarks (dataset or algorithm) in many
researches emphasizes the need for a more robust algorithmic solution. Through
this research, we aim to give performance comparative analysis of various
underwater trash detection algorithms.
