Automating Jellyfish Species Recognition through Faster Region-Based Convolution Neural Networks

Adam Gauci,John Abela,Alan Deidun

doi:10.3390/app10228257

Abstract

In recent years, citizen science campaigns have provided a very good platform for widespread data collection. Within the marine domain, jellyfish are among the most commonly deployed species for citizen reporting purposes. The timely validation of submitted jellyfish reports remains challenging, given the sheer volume of reports being submitted and the relative paucity of trained staff familiar with the taxonomic identification of jellyfish. In this work, hundreds of photos that were submitted to the “Spot the Jellyfish” initiative are used to train a group of region-based, convolution neural networks. The main aim is to develop models that can classify, and distinguish between, the five most commonly recorded species of jellyfish within Maltese waters. In particular, images of the Pelagia noctiluca, Cotylorhiza tuberculata, Carybdea marsupialis, Velella velella and salps were considered. The reliability of the digital architecture is quantified through the precision, recall, f1 score, and κ score metrics. Improvements gained through the applicability of data augmentation and transfer learning techniques, are also discussed. Very promising results, that support upcoming aspirations to embed automated classification methods within online services, including smart phone apps, were obtained. These can reduce, and potentially eliminate, the need for human expert intervention in validating citizen science reports for the five jellyfish species in question, thus providing prompt feedback to the citizen scientist submitting the report.

Highlights

Citizen science incorporates scientific research and monitoring projects for which members of the public collect, categorise, transcribe or analyse scientific data
Citizen science has definitely come of age in recent years in generating knowledge, creating new learning opportunities and enabling civic participation [1]
These protocols have been incorporated within fixed-location, jellyfish bloom early-warning systems, including the JellyMonitor prototype deployed on the Scottish seabed and which makes use of both classic computer vision and deep learning neural networks to detect the occurrence of jellyfish blooms [9]

Summary

Introduction

Citizen science incorporates scientific research and monitoring projects for which members of the public collect, categorise, transcribe or analyse scientific data. Given the image-intensive nature of many ongoing marine citizen science campaigns, the image analyses domains of Artificial Intelligence and Machine Learning have increasingly been applied to automate the validation of the same images [8] and to expedite the normally lengthy process These protocols have been incorporated within fixed-location, jellyfish bloom early-warning systems, including the JellyMonitor prototype deployed on the Scottish seabed and which makes use of both classic computer vision and deep learning neural networks to detect the occurrence of jellyfish blooms [9]. Campaign website as well as on a mobile phone app This service will simplify and speed up the report validation process drastically, providing a prompt response to citizen scientists on the taxonomic identity of the recorded species, and this is the main motivation behind this exercise. By facilitating the automated extraction and interpretation of information from submitted images, and the report validation process, the same algorithm can make a positive contribution by improving the efficacy and attractiveness of the same campaigns

Image Dataset

Classification Model

Results

Conclusions