TUD-GV Dataset for Floating Litter Detection (object detection task)

Dataset

This dataset contains the data used for the publication:

Jia, T., de Vries, R., Kapelan, Z., van Emmerik, T. H., & Taormina, R. (2024). Detecting floating litter in freshwater bodies with semi-supervised deep learning. Water Research, 266, 122405. https://doi.org/10.1016/j.watres.2024.122405

This dataset is a subset of the large-scale "TU Delft - Green Village" (TUD-GV), which includes 9,473 RGB images. More details on the TUD-GV dataset can be found at: https://doi.org/10.5281/zenodo.7636124. We construct this subset by selecting 1,501 images from the full TUD-GV dataset, and annotating the litter items in these images with bounding boxes, resulting in 8181 annotations. This subset is specifically for detecting floating litter in object detection tasks.

The 1,501 images are stored in the images.zip file, the annotations are stored in the labels_txt.zip file, and the class of the annotation (i.e., litter) is stored in the classes.txt file.

Cite this dataset

If you use this dataset for a publication, please cite the paper. Here is a BibTeX entry:

@article{jia2024detecting,
title={Detecting floating litter in freshwater bodies with semi-supervised deep learning},
author={Jia, Tianlong and de Vries, Rinze and Kapelan, Zoran and van Emmerik, Tim HM and Taormina, Riccardo},
journal={Water Research},
volume={266},
pages={122405},
year={2024},
publisher={Elsevier}
}