Pubblicazione scientifica

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

FILONE TECNOLOGICO	TEMA
economia circolare e sostenibilità

RIASSUNTO

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.

DATA

Data di pubblicazione: 19/03/2020

AUTORI

MARTIN-ABADAL MIGUEL
RUIZ-FRAU ANA
HINZ HILMAR
GONZALEZ-CID YOLANDA

ENTE DI AFFERENZA

DEPT MATHEMATICS & COMPUTER SCIENCE SYS ROBOTICS & VISION GROUP SRV UNIV LES ILLES BALEARS 07122 PALMA SPAIN YOLANDA GONZALEZ UIB ES
DEPARTMENT OF MARINE ECOSYSTEM DYNAMICS IMEDEA CSIC-UIB INSTITUT MEDITERRANI D’ESTUDIS AVANCATS 07190 ESPORLES SPAIN ANARUIZ@IMEDEA UIB-CSIC ES AR -F HHINZ@IMEDEA UIB-CSIC ES HH

RIVISTA

Sensors (Basel, Switzerland), 20(6), 1708

IMPACT FACTOR

3,510

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