The Marine Debris Forward-Looking Sonar Datasets

Fuente: arXiv
Enregistré dans:
Détails bibliographiques
Auteurs principaux: Valdenegro-Toro, Matias, Padmanabhan, Deepan Chakravarthi, Singh, Deepak, Wehbe, Bilal, Petillot, Yvan
Format: Preprint
Publié: 2025
Sujets:
Accès en ligne:
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
_version_ 1866915218548850688
author Valdenegro-Toro, Matias
Padmanabhan, Deepan Chakravarthi
Singh, Deepak
Wehbe, Bilal
Petillot, Yvan
author_facet Valdenegro-Toro, Matias
Padmanabhan, Deepan Chakravarthi
Singh, Deepak
Wehbe, Bilal
Petillot, Yvan
contents Sonar sensing is fundamental for underwater robotics, but limited by capabilities of AI systems, which need large training datasets. Public data in sonar modalities is lacking. This paper presents the Marine Debris Forward-Looking Sonar datasets, with three different settings (watertank, turntable, flooded quarry) increasing dataset diversity and multiple computer vision tasks: object classification, object detection, semantic segmentation, patch matching, and unsupervised learning. We provide full dataset description, basic analysis and initial results for some tasks. We expect the research community will benefit from this dataset, which is publicly available at https://doi.org/10.5281/zenodo.15101686
format Preprint
id arxiv_https___arxiv_org_abs_2503_22880
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle The Marine Debris Forward-Looking Sonar Datasets
Valdenegro-Toro, Matias
Padmanabhan, Deepan Chakravarthi
Singh, Deepak
Wehbe, Bilal
Petillot, Yvan
Computer Vision and Pattern Recognition
Machine Learning
Sonar sensing is fundamental for underwater robotics, but limited by capabilities of AI systems, which need large training datasets. Public data in sonar modalities is lacking. This paper presents the Marine Debris Forward-Looking Sonar datasets, with three different settings (watertank, turntable, flooded quarry) increasing dataset diversity and multiple computer vision tasks: object classification, object detection, semantic segmentation, patch matching, and unsupervised learning. We provide full dataset description, basic analysis and initial results for some tasks. We expect the research community will benefit from this dataset, which is publicly available at https://doi.org/10.5281/zenodo.15101686
title The Marine Debris Forward-Looking Sonar Datasets
topic Computer Vision and Pattern Recognition
Machine Learning
url https://arxiv.org/abs/2503.22880