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sub:assertion {
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    <http://schema.org/dateModified> "2025-10-14 08:34:02.569465+00:00";
    <http://schema.org/description> "The robotic seabed cleaning platform developed by TECNALIA, CNRS- LIRMM and “Servizi Tecnici”, consists in a floating platform which, through cables and winches, the seabed cleaning robot is attached. The structure is equipped with a set of sensors for underwater perception to control the robot and detect & identify the marine litter to be removed. Moreover, the robotic platform is characterized by two different tools that allow to collect the ML on the seabed: a drudge to suck up smaller litter and a gripper to grasps larger items like tires, parts of boats, fishing nets etc.";
    <http://schema.org/license> <https://creativecommons.org/licenses/by/4.0/legalcode>;
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    <http://schema.org/creator> <mailto:taha.lahami@ve.ismar.cnr.it>;
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    <http://schema.org/dateModified> "2025-10-14 08:34:03.226349+00:00";
    <http://schema.org/description> """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""";
    <http://schema.org/license> <https://creativecommons.org/licenses/by/4.0/legalcode>;
    <http://schema.org/name> """From TrashCan to UNO: Deriving an Underwater Image
 Dataset To Get a More Consistent and Balanced Version""";
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    <http://schema.org/description> """As a contribution to the development of new techniques to
 remove marine litter from the seabed of sees and oceans, the Robotic
 Seabed Cleaning Platform has been designed, built and experimented
 in the framework of the European Union project MAELSTROM. It es
sentially consists of a oating platform that supports the base elements
 of a 6 degree-of-freedom cable-driven parallel robot actuated by eight
 winches. The mobile platform of this robot can work underwater and is
 equipped with sensors to control its underwater motions and to detect &
 identify marine litter. To achieve e cient and selective litter removal, an
 aspiration system and a gripper are installed on the CDPR underwater
 mobile platform.""";
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 Underwater Cable-Driven Parallel Robot for
 Marine Litter Removal""";
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