Inspection Operations and Hole Detection in Fish Net Cages through a Hybrid Underwater Intervention System Using Deep Learning Techniques
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Otros documentos de la autoría: López Barajas, Salvador; Sanz, Pedro J; Marin, Raul; Gómez-Espinosa, Alfonso; González-García, Josué; Echagüe, Juan
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Título
Inspection Operations and Hole Detection in Fish Net Cages through a Hybrid Underwater Intervention System Using Deep Learning TechniquesAutoría
Fecha de publicación
2023-12-29Editor
MDPIISSN
2077-1312Cita bibliográfica
López-Barajas, Salvador, Pedro J. Sanz, Raúl Marín-Prades, Alfonso Gómez-Espinosa, Josué González-García, and Juan Echagüe. 2024. "Inspection Operations and Hole Detection in Fish Net Cages through a Hybrid Underwater Intervention System Using Deep Learning Techniques" Journal of Marine Science and Engineering 12, no. 1: 80. https://doi.org/10.3390/jmse12010080Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Net inspection in fish-farm cages is a daily task for divers. This task represents a high cost for fish farms and is a high-risk activity for human operators. The total inspection surface can be more than 1500 m2
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Net inspection in fish-farm cages is a daily task for divers. This task represents a high cost for fish farms and is a high-risk activity for human operators. The total inspection surface can be more than 1500 m2
, which means that this activity is time-consuming. Taking into account the severe restrictions for human operators in such hostile underwater conditions, this activity represents a significant area for improvement. A platform for net inspection is proposed in this work. This platform includes a surface vehicle, a ground control station, and an underwater vehicle (BlueROV2 heavy) which incorporates artificial intelligence, trajectory control procedures, and the necessary communications. In this platform, computer vision was integrated, involving a convolutional neural network trained to predict the distance between the net and the robot. Additionally, an object detection algorithm was developed to recognize holes in the net. Furthermore, a simulation environment was established to evaluate the inspection trajectory algorithms. Tests were also conducted to evaluate how underwater wireless communications perform in this underwater scenario. Experimental results about the hole detection, net distance estimation, and the inspection trajectories demonstrated robustness, usability, and viability of the proposed methodology. The experimental validation took place in the CIRTESU tank, which has dimensions of 12 × 8 × 5 m, at Universitat Jaume I. [-]
Publicado en
J. Mar. Sci. Eng. 2024, 12, 80. https://doi.org/10.3390/jmse12010080Datos relacionados
Hole detection dataset available at https://app.roboflow.com/salvador-lpez-barajas/realholes/2 accessed on 24 December 2023.Entidad financiadora
Ministerio de Ciencia, Innovación y Universidades | NextGenerationEU PRTR-C17.I1 | Generalitat Valenciana
Código del proyecto o subvención
PDC2021-120791-C22 | ThinkInAzul/2021/037
Derechos de acceso
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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