Methods for long‐term monitoring of coastal species such as harbor seals ( Phoca vitulina ) are often costly, time‐consuming, and highly invasive, underscoring the need for improved techniques for data collection and analysis. Here, we propose the use of automated facial recognition technology for identification of individual seals and demonstrate its utility in ecological and population studies. We created a software package, SealNet, that automates photo identification of seals, using a graphical user interface (GUI) software to detect, align, and chip seal faces from photographs and a deep convolutional neural network (CNN) suitable for small datasets (e.g., 100 seals with five photos per seal) to classify individual seals. We piloted the SealNet technology with a population of harbor seals located within Casco Bay on the coast of Maine, USA. Across two years of sampling, 2019 and 2020, at seven haul‐out sites in Middle Bay, we obtained a dataset optimized for the development and testing of SealNet. We processed 1752 images representing 408 individual seals and achieved 88% Rank‐1 and 96% Rank‐5 accuracy in closed set seal identification. In identifying individual seals, SealNet software outperformed a similar face recognition method, PrimNet, developed for primates but retrained on seals. The ease and wealth of image data that can be processed using SealNet software contributes a vital tool for ecological and behavioral studies of marine mammals in the developing field of conservation technology.
Methods for long-term monitoring of coastal species such as harbor seals, are often costly, time-consuming, and highly invasive, underscoring the need for improved techniques for data collection and analysis. Here, we propose the use of automated facial recognition technology for identification of individual seals and demonstrate its utility in ecological and population studies. We created a software package, SealNet, that automates photo identification of seals, using a graphical user interface (GUI) software to identify, align and chip seal faces from photographs and a deep convolutional neural network (CNN) suitable for small datasets (e.g., 100 seals with five photos per seal). We piloted the SealNet technology with a population of harbor seals located within Casco Bay on the coast of Maine, USA. Across two-years of sampling, 2019 and 2020, at seven haul-out sites in Middle Bay, we processed 1529 images representing 408 individual seals and achieved 88% (93%) rank-1 accuracy in closed set (open set) seal identification. We identified four seals that were photographed in both years at neighboring haul-out sites, suggesting that some harbor seals exhibit site fidelity within local bays across years, and that there may be evidence of spatial connectivity among haul-out sites. Using capture-mark-recapture (CMR) calculations, we obtained a rough preliminary population estimate of 4386 seals in the Middle Bay area. SealNet software outperformed a similar face recognition method developed for primates, PrimNet, in identifying seals following training on our seal dataset. The ease and wealth of image data that can be processed using SealNet software contributes a vital tool for ecological and behavioral studies of marine mammals in the emerging field of conservation technology.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.