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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://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/cdd7e89f-7013-45cd-9d15-00c4d7fe19fa http://schema.org/license https://creativecommons.org/licenses/by/4.0/legalcode http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/cdd7e89f-7013-45cd-9d15-00c4d7fe19fa http://schema.org/name Robotic seabed cleaning platform - video http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/cdd7e89f-7013-45cd-9d15-00c4d7fe19fa http://schema.org/sdDatePublished 2025-06-11 09:21:53.348421+00:00 http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/cdd7e89f-7013-45cd-9d15-00c4d7fe19fa http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/cdd7e89f-7013-45cd-9d15-00c4d7fe19fa http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject http://seamap-catalog.data.ismar.cnr.it:8080/geonetwork/maelstrom/ita/catalog.search#/metadata/cdd7e89f-7013-45cd-9d15-00c4d7fe19fa http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/VideoObject http://www.ismar.cnr.it/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Organization https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document http://schema.org/author mailto:taha.lahami@ve.ismar.cnr.it https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document http://schema.org/contentUrl https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document http://schema.org/creator mailto:taha.lahami@ve.ismar.cnr.it https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document http://schema.org/dateCreated 2025-06-11 11:05:31.499118+00:00 https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document http://schema.org/dateModified 2025-10-14 08:34:03.226349+00:00 https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document 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 https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document http://schema.org/license https://creativecommons.org/licenses/by/4.0/legalcode https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document http://schema.org/name From TrashCan to UNO: Deriving an Underwater Image Dataset To Get a More Consistent and Balanced Version https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document http://schema.org/sdDatePublished 2025-06-11 11:05:31.499118+00:00 https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/roterms#Paper https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://hal-lirmm.ccsd.cnrs.fr/lirmm-03815628/document http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://hal-lirmm.ccsd.cnrs.fr/lirmm-04261099v1/document http://schema.org/author mailto:taha.lahami@ve.ismar.cnr.it https://hal-lirmm.ccsd.cnrs.fr/lirmm-04261099v1/document http://schema.org/contentUrl https://hal-lirmm.ccsd.cnrs.fr/lirmm-04261099v1/document https://hal-lirmm.ccsd.cnrs.fr/lirmm-04261099v1/document http://schema.org/creator mailto:taha.lahami@ve.ismar.cnr.it https://hal-lirmm.ccsd.cnrs.fr/lirmm-04261099v1/document http://schema.org/dateCreated 2025-06-11 11:00:03.950011+00:00 https://hal-lirmm.ccsd.cnrs.fr/lirmm-04261099v1/document http://schema.org/dateModified 2025-10-14 08:34:03.540183+00:00 https://hal-lirmm.ccsd.cnrs.fr/lirmm-04261099v1/document 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. 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11:37:10.557566+00:00 https://w3id.org/ro-id/5c2b7b95-a99c-4489-b0ac-f656a65b90f7/ http://schema.org/datePublished 2025-05-28 09:48:38.233944+00:00 https://w3id.org/ro-id/5c2b7b95-a99c-4489-b0ac-f656a65b90f7/ http://schema.org/description This objective supports the implementation of the UN SDG 14 Life Below Water, by addressing two major challenges: (i)Surface and Upper Water Column litter: Floating plastics carried by rivers and harbours currents must be intercepted and captured before they enter the oceans and either sink to the seabed or create gyres MAELSTROM will deploy an automated air-bubbles barrier installed in optimized locations to retrieve these floating items, and also contribute to the re-oxygenation of the rivers, harbours and lagoons. 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Grant agreement No 101000832. https://doi.org/10.3030/101000832. https://w3id.org/ro-id/5c2b7b95-a99c-4489-b0ac-f656a65b90f7/ https://www.w3.org/ns/iana/link-relations/relation#cite-as Fantina Madricardo, Vanessa Moschino, ANTONIO PETRIZZO, Taha Lahami, and Valentina Grande. "MAELSTROM Objective 2: ML Removal from seabed and both lower and upper water column - archive." ROHub. 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(2022). D5.1 Report on site identification and installation of the Seabed Cleaning System in Venice. 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(2024). D3.3 Preliminary report on the Cable robot autonomous control using Machine learning for litter identification. 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(2022). D3.1 Report on the cable robot design and teleoperated control. 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A., Nogueira, S., Bio, A., Sousa-Pinto, I., Buschman, F. A., Mira Veiga, J., Pessoa, A., Zingariello, D., & Mule'Stagno, L. (2025). D5.4 Final Report on operation of the surface and water column removal technology in the Porto region. 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O., Petrizzo, A., MOSCHINO, V., Mesghez, S., Lahami, T., Lorenzetti, G., & Madricardo, F. (2025). D5.2 Final report on operation of the Seabed Cleaning System in Venice. 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