Advances in deep learning-driven photo identification and meta analysis of cetaceans in large data repositories

Alexander Barnhill, Jared R. Towers, Tasli J.H. Shaw, Magdalena Arias, Adrián Bécares, Thomas Doniol-Valcroze, Lorenzo von Fersen, Rodrigo Genoves, Tim Rörup, Gary J. Sutton, Sheila Thornton, Michael Weiss, Andreas Maier, Elmar Nöth, Christian Bergler,
Advances in deep learning-driven photo identification and meta analysis of cetaceans in large data repositories,
Ecological Informatics,
Volume 91,
2025,
103396,
ISSN 1574-9541,
https://doi.org/10.1016/j.ecoinf.2025.103396.
(https://www.sciencedirect.com/science/article/pii/S1574954125004054)
Abstract: Photo-identification of cetaceans remains a labor-intensive task, requiring expert annotation of long-tailed image datasets in which most individuals are rarely encountered. We present a scalable, end-to-end framework that automates this process using lightweight deep learning models optimized for resource-constrained environments. Our modular pipeline integrates state-of-the-art detection (YOLOv8-small), individual identification via metric learning (EfficientNet-B0 with a contrastive head), and auxiliary modules for image quality scoring, side classification, and identifiability prediction. Unlike previous approaches limited to single-species applications or high-resource settings, our framework generalizes across five cetacean populations with diverse visual characteristics. We achieve top-1 identification accuracies of 0.92 for Bigg's killer whales (Orcinus orca rectipinnus), 0.96 for Southern resident killer whales (Orcinus orca ater), 0.96 for Lahille's bottlenose dolphins (Tursiops truncatus gephyreus), 0.82 for common minke whales (Balaenoptera acutorostrata scammoni), and 0.85 for humpback whales (Megaptera novaeangliae), yielding a cross-species accuracy of 0.90. To support image triage in large datasets, we include a quality scoring module that predicts image utility using learned embedding features. This module achieves an R2 of 0.799, enabling intelligent prioritization of data. Runtime evaluations show processing speeds of 1.6–3.2 images/s on CPU and 9.6–23.3 FPS with GPU acceleration, making it suitable for archival and real-time applications. We also evaluate the impact of demographic metadata (age, sex) on identification performance and provide practical recommendations for future dataset design. The system is available via a web interface designed to support real-world conservation workflows with minimal computational overhead.
Keywords: Deep learning; Photo identification; Marine conservation; Data curation; Resource efficient machine learning