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Open AccessArticle

Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection

1
Department of Mathematics and Computer Science, Systems Robotics and Vision Group (SRV), Universitat de les Illes Balears, 07122 Palma, Spain
2
Department of Marine Ecosystem Dynamics, IMEDEA (CSIC-UIB), Institut Mediterrani d’Estudis Avançats, 07190 Esporles, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1708; https://doi.org/10.3390/s20061708
Received: 31 January 2020 / Revised: 13 March 2020 / Accepted: 17 March 2020 / Published: 19 March 2020
(This article belongs to the Special Issue Imaging Sensor Systems for Analyzing Subsea Environment and Life)
During the past decades, the composition and distribution of marine species have changed due to multiple anthropogenic pressures. Monitoring these changes in a cost-effective manner is of high relevance to assess the environmental status and evaluate the effectiveness of management measures. In particular, recent studies point to a rise of jellyfish populations on a global scale, negatively affecting diverse marine sectors like commercial fishing or the tourism industry. Past monitoring efforts using underwater video observations tended to be time-consuming and costly due to human-based data processing. In this paper, we present Jellytoring, a system to automatically detect and quantify different species of jellyfish based on a deep object detection neural network, allowing us to automatically record jellyfish presence during long periods of time. Jellytoring demonstrates outstanding performance on the jellyfish detection task, reaching an F1 score of 95.2%; and also on the jellyfish quantification task, as it correctly quantifies the number and class of jellyfish on a real-time processed video sequence up to a 93.8% of its duration. The results of this study are encouraging and provide the means towards a efficient way to monitor jellyfish, which can be used for the development of a jellyfish early-warning system, providing highly valuable information for marine biologists and contributing to the reduction of jellyfish impacts on humans. View Full-Text
Keywords: deep learning; object detection; jellyfish quantification; jellyfish monitoring deep learning; object detection; jellyfish quantification; jellyfish monitoring
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MDPI and ACS Style

Martin-Abadal, M.; Ruiz-Frau, A.; Hinz, H.; Gonzalez-Cid, Y. Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection. Sensors 2020, 20, 1708.

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