Wildlife biologists increasingly use camera traps for monitoring animal populations. However,
manually sifting through the collected images is expensive and time-consuming. Current deep learning
studies for camera trap images do not adequately tackle real-world challenges such as imbalances
between animal and empty images, distinguishing similar species, and the impact of backgrounds on
species identification, limiting the models’ applicability in new locations. Here, we present a novel
two-stage deep learning framework. First, we train a global deep-learning model using all animal
species in the dataset. Then, an agglomerative clustering algorithm groups animals based on their
appearance. Subsequently, we train a specialized deep-learning expert model for each animal group to
detect similar features. This approach leverages Transfer Learning from the MegaDetectorV5 (YOLOv5
version) model, already pre-trained on various animal species and ecosystems. Our two-stage deep
learning pipeline uses the global model to redirect images to the appropriate expert models for final
classification. We validated this strategy using 1.3 million images from 91 camera traps encompassing
24 mammal species and used 120,000 images for testing, achieving an F1-Score of 96.2% using expert
models for final classification. This method surpasses existing deep learning models, demonstrating
improved precision and effectiveness in automated wildlife detection.
