Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats
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Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats Ellen M. Ditria & Michael Sievers & Sebastian Lopez-Marcano & Eric L. Jinks & Rod M. Connolly
Received: 29 April 2020 / Accepted: 30 September 2020 # Springer Nature Switzerland AG 2020
Abstract Environmental monitoring guides conservation and is particularly important for aquatic habitats which are heavily impacted by human activities. Underwater cameras and uncrewed devices monitor aquatic wildlife, but manual processing of footage is a significant bottleneck to rapid data processing and dissemination of results. Deep learning has emerged as a solution, but its ability to accurately detect animals across habitat types and locations is largely untested for coastal environments. Here, we produce five deep learning models using an object detection framework to detect an ecologically important fish, luderick (Girella tricuspidata). We trained two models on footage from single habitats (seagrass or reef) and three on footage from both habitats. All models were subjected to tests from both habitat types. Models performed well on test data from the same habitat type (object detection measure: mAP50: 91.7 and 86.9% performance for seagrass and reef, respectively) but poorly on test sets from a different habitat type (73.3 and 58.4%, respectively). The model trained on a combination of both habitats produced the highest object detection results for both tests (an average of 92.4 and 87.8%, respectively). The ability of the combination Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10661-020-08653-z) contains supplementary material, which is available to authorized users. E. M. Ditria (*) : M. Sievers : S. Lopez-Marcano : E. L. Jinks : R. M. Connolly Australian Rivers Institute – Coast & Estuaries, and School of Environment and Science, Griffith University, Gold Coast, QLD 4222, Australia e-mail: [email protected]
trained models to correctly estimate the ecological abundance metric, MaxN, showed similar patterns. The findings demonstrate that deep learning models extract ecologically useful information from video footage accurately and consistently and can perform across habitat types when trained on footage from the variety of habitat types. Keywords Computer vision . Machine learning . MaxN . Monitoring . Reef . Seagrass
Introduction People have been monitoring and counting wildlife for millennia, collecting invaluable data for several uses such as informing conservation, tracking population trends, and estimating abundance or biomass for fisheries stock assessments (Goldsmith 2012). As the world changes and ecosystems experience severe and sustained declines in extent and condition (Maxwell et al. 2016), monitoring wildlife has never been more important. The speed and scale at which the natural world is changing also mean that monitoring and analysing data quickly enough to be able to respond has become a global challenge. Aquatic coastal habitats are
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