Underwater Trash Detection Dataset

Overview

The Underwater Trash Detection Dataset is a custom-annotated dataset designed to address the challenges of underwater trash detection caused by varying environmental features. Publicly available datasets alone are insufficient for training deep learning models due to domain-specific variations in underwater conditions. This dataset offers a cumulative, self-annotated collection of underwater images for detecting and classifying trash, providing a strong foundation for deep learning research and benchmark testing.

Dataset Summary

Total Images: 9,576
Annotation Types: Trash classification (plastic, trash, underwater debris) vs. environmental factors (fish, flora, fauna).

Dataset Split

Split	Percentage	Number of Images
Train Set	76%	7,308
Validation	19%	1,795
Test Set	5%	473

Preprocessing

Image Resize: All images are resized to 256x256 pixels using stretching for uniform input dimensions.

Purpose

This dataset supports research in underwater trash detection while addressing storage and computational constraints in underwater mobile devices. It enables the development of optimized algorithms for efficient trash detection and classification using minimal resources.

Citation

If you use this dataset in your research, please cite:

@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",
}