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

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 R 2 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. • Modular AI pipeline filters, detects, scores, and identifies cetaceans step-by-step • Quality and orientation classifiers remove poor or unusable detections early and allow triaging of unlabelled datasets • Each step is lightweight and runs efficiently on modest hardware setups • Combines YOLOv8, EfficientNet, and contrastive learning for identification • Enables scalable, automated ID across large, heterogeneous image corpora