WILDetect - Part I

Kuru, Kaya orcid iconORCID: 0000-0002-4279-4166, Clough, Stuart, Ansell, Darren orcid iconORCID: 0000-0003-2818-3315, McCarthy, John and McGovern, Stephanie (2024) WILDetect - Part I. Coordinates, 20 (5). pp. 19-29. ISSN 0973-2136

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A new non-parametric approach, WILDetect, has been built using an ensemble of supervised Machine Learning (ML) and Reinforcement Learning (RL) techniques. We present here the first part of the paper. The concluding part will be published in the next issue. The habitats of marine life, characteristics of species, and the diverse mix of maritime industries around these habitats are of interest to many researchers, authorities, and policymakers whose aim is to conserve the earth’s biological diversity in an ecologically sustainable manner while being in line with indispensable industrial developments. Automated detection, locating, and monitoring of marine life along with the industry around the habitats of this ecosystem may be helpful to (i) reveal current impacts, (ii) model future possible ecological trends, and (iii) determine required policies which would lead accordingly to a reduced ecological footprint and increased sustainability. New automatic techniques are required to observe this large environment efficiently. Within this context, this study aims to develop a novel platform to monitor marine ecosystems and perform bio census in an automated manner, particularly for birds in regional aerial surveys since birds are a good indicator of overall ecological health. In this manner, a new non-parametric approach, WILDetect, has been built using an ensemble of supervised Machine Learning (ML) and Reinforcement Learning (RL) techniques. It employs several hybrid techniques to segment, split and count maritime species — in particular, birds — in order to perform automated censuses in a highly dynamic marine ecosystem. The efficacy of the proposed approach is demonstrated by experiments performed on 26 surveys which include Northern gannets (Morus bassanus) by utilising retrospective data analysis techniques. With this platform, by combining multiple techniques, gannets can be detected and split automatically with very high sensitivity (Se) (0.97), specificity (Sp) (0.99), and accuracy (Acc) (0.99) — these values are validated by precision (Pr) (0.98). Moreover, the evaluation of the system by the APEM staff, which uses a completely new evaluation dataset gathered from recent surveys, shows the viability of the proposed techniques. The experimental results suggest that similar automated data processing techniques — tailored for specific species — can be helpful both in performing time-intensive marine wildlife censuses efficiently and in establishing ecological platforms/models to understand the underlying causes of trends in species populations along with the ecological change.

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