Due to highly variying domain features of different underwater enviornment, the publically available datasets alone are not the best fit to train a deep learning algorithm to predict trash. Therefore we propose a cumulatuve, self-annonated dataset that provides a good foundation for training models to detect and classify trash underwater, and also provide benchmarks for the same. This repository aims to generate code that detects trash, classifies it into plastic / trash / underwater debris against other factors like fish / flora & fauna and input rover images.
Aiming at the problem of insufficient storage space and limited computing ability of underwater mobile devices, an underwater garbage detection algorithm is proposed.
result.mp4
@misc{walia2024deeplearninginnovationsunderwater,
title={Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysis},
author={Jaskaran Singh Walia and Pavithra L K},
year={2024},
eprint={2405.18299},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2405.18299},
}
@InProceedings{10.1007/978-3-031-43360-3_24,
author="Walia, Jaskaran Singh
and Seemakurthy, Karthik",
editor="Iida, Fumiya
and Maiolino, Perla
and Abdulali, Arsen
and Wang, Mingfeng",
title="Optimized Custom Dataset for Efficient Detection of Underwater Trash",
booktitle="Towards Autonomous Robotic Systems",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="292--303",
}
