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Article

Inspection Operations and Hole Detection in Fish Net Cages through a Hybrid Underwater Intervention System Using Deep Learning Techniques †

by
Salvador López-Barajas
1,2,3,*,
Pedro J. Sanz
1,
Raúl Marín-Prades
1,
Alfonso Gómez-Espinosa
2,*,
Josué González-García
2 and
Juan Echagüe
1
1
Interactive Robotic Systems Lab, Jaume I University, 12071 Castellón de la Plana, Spain
2
Escuela de Ingenieria y Ciencias, Tecnologico de Monterrey, Av. Epigmenio González 500, Fracc. San Pablo, Queretaro 76130, Mexico
3
ValgrAI—Valencian Graduate School and Research Network for Artificial Intelligence, Camí de Vera S/N, Edificio 3Q, 46022 Valencia, Spain
*
Authors to whom correspondence should be addressed.
†
This paper is an extended version of our conference paper “Automatic Visual Inspection of a Net for Fish Farms by Means of Robotic Intelligence”. In Proceedings of the OCEANS 2023-Limerick, Limerick, Ireland, 5–8 June 2023.
J. Mar. Sci. Eng. 2024, 12(1), 80; https://doi.org/10.3390/jmse12010080
Submission received: 24 November 2023 / Revised: 20 December 2023 / Accepted: 25 December 2023 / Published: 29 December 2023
(This article belongs to the Special Issue Advances in Underwater Robots for Intervention)
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Abstract

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.
Keywords: autonomous underwater vehicle; surface vehicle; convolutional neural networks; underwater inspection; aquaculture autonomous underwater vehicle; surface vehicle; convolutional neural networks; underwater inspection; aquaculture

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MDPI and ACS Style

López-Barajas, S.; Sanz, P.J.; Marín-Prades, R.; Gómez-Espinosa, A.; González-García, J.; Echagüe, J. Inspection Operations and Hole Detection in Fish Net Cages through a Hybrid Underwater Intervention System Using Deep Learning Techniques. J. Mar. Sci. Eng. 2024, 12, 80. https://doi.org/10.3390/jmse12010080

AMA Style

López-Barajas S, Sanz PJ, Marín-Prades R, Gómez-Espinosa A, González-García J, Echagüe J. 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. 2024; 12(1):80. https://doi.org/10.3390/jmse12010080

Chicago/Turabian Style

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/jmse12010080

APA Style

López-Barajas, S., Sanz, P. J., Marín-Prades, R., Gómez-Espinosa, A., González-García, J., & Echagüe, J. (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(1), 80. https://doi.org/10.3390/jmse12010080

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