A Comparison of Convolutional Neural Network Architectures for Automated Detection and Identification of Waterfowl in Complex Environments

Waterfowl monitoring is an important task for understanding waterfowl distribution and habitats. Surveying approaches using hyper-spatial airborne imagery, collected by small unoccupied aerial systems (sUAS), hold potential to overcome the limitations of traditional methods while improving count efficiency and reliability. Difficulties obtaining waterfowl counts, particularly in complex image scenes, from the high quantity of imagery required hinders deployment of large-scale surveys. In this paper, we test Convolutional Neural Networks (CNNs) to understand their potential and how they behave across different versions of our waterfowl dataset. Three CNN architectures (YOLO, Retinanet and Faster RCNN) were trained on 3 hierarchical levels: waterfowl detection (True / False), waterfowl type (3 classes), and waterfowl species (8 classes). The architectures generally performed well, and results indicate that automated waterfowl detection in complex environments, and therefore enumeration, is feasible using current technology. Waterfowl identification in complex environments was not successful using the available training data, but we propose steps that might enhance the results.