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

Miguel Martin-Abadal,Ana Ruiz-Frau,Hilmar Hinz,Yolanda Gonzalez-Cid

doi:10.3390/s20061708

Abstract

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.

Highlights

During the past decades, the marine environment has been under increased pressure by human activities, such as the over-exploitation of marine species [1], the destruction and modifications of habitats [2], the introduction of alien species [3], as well as pollution [4] and human-induced climate change [5,6]
The detection and quantification of changes in marine species are of vital importance to monitor environmental status and its change over time, in particular, the benefits society derives from ecosystems, known as ecosystem services [8]
We present Jellytoring, a system to automatically detect and quantify different species of jellyfish based on a deep object detection neural network

Summary

Introduction

The marine environment has been under increased pressure by human activities, such as the over-exploitation of marine species [1], the destruction and modifications of habitats [2], the introduction of alien species [3], as well as pollution [4] and human-induced climate change [5,6]. These pressures have caused highly relevant changes in the composition and distribution of marine organisms [7]. Video observations have been processed and classified by human observers, which in many instances is time-consuming and financially costly [9,10]

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Conclusion