Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysis
<|reference_start|>Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysis: 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.<|reference_end|>
@article{walia2024deep,
    title={Deep Learning Innovations for Underwater Waste Detection: An In-Depth
  Analysis},
    author={Jaskaran Singh Walia and Pavithra L K and Kesar Mehta and Shivram
  Harshavardhana and Nandini Tyagi},
    journal={arXiv preprint arXiv:2405.18299},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18299},
    primaryClass={cs.CV cs.AI cs.LG cs.RO}
}