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Full identifier: https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document
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From TrashCan to UNO: Deriving an Underwater Image
Dataset To Get a More Consistent and Balanced Version
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The multiplication of publicly available datasets makes it
possible to develop Deep Learning models for many real-world applica
tions. However, some domains are still poorly explored, and their related
datasets are often small or inconsistent. In addition, some biases linked
to the dataset construction or labeling may give the impression that a
model is particularly efficient. Therefore, evaluating a model requires a
clear understanding of the database. Moreover, a model often reflects a
given dataset’s performance and may deteriorate if a shift exists between
the training dataset and real-world data.
In this paper, we derive a more consistent and balanced version of the
TrashCan [6] image dataset, called UNO, to evaluate models for de
tecting non-natural objects in the underwater environment. We pro
pose a method to balance the number of annotations and images for
cross-evaluation. We then compare the performance of a SOTA object
detection model when using TrashCAN and UNO datasets. Addition
ally, we assess covariate shift by testing the model on an image dataset
for real-world application. Experimental results show significantly better
and more consistent performance using the UNO dataset.
The UNO database and the code are publicly av
https://github.com/CBarrelet/balanced_kfold
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2025-10-14 08:34:03.226349+00:00
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