Browsing by Author "Ball, Samantha"

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Harnessing unlabelled data for automatic aerial poacher detection: reducing annotation costs through unsupervised and self-supervised learning
(2024) Ball, Samantha
The recent escalation in wildlife poaching poses a major threat to the survival of several key species, with South Africa and Zimbabwe forming the epicentre of the poaching crisis. The application of emerging technology such as Unmanned Aerial Vehicles (UAVs) and object detection provides a novel way of tackling this issue through the use of aerial surveillance. However, despite pioneering studies into the practical use of computer vision for poacher detection, current models require detailed ground truth. Notably, the sparsity and small-scale of objects in poaching detection data renders the annotation process particularly expensive and time-consuming, posing a barrier to resource-constrained conservation organisations in real-world scenarios. To reduce the need for costly annotations, this study explores the use of the self-supervised DINO model and unsupervised anomaly detection network FastFlow to provide pseudo-labels for unlabelled data. The value of these alternative techniques is evaluated on real-world poaching detection data provided by a Southern African conservation NPO. The results indicate that a YOLOv5 detection model can be trained using pseudo-labels together with only a small fraction of manually-annotated ground truth for the most difficult training videos. The resulting models attain over 90% of the detection recall of a baseline model trained with original ground truth labels, while also maintaining real-time detection speeds. This reduction in annotation cost would allow current systems to harness large unlabelled datasets with greatly reduced annotation effort and time, while still meeting the efficiency constraints associated with the UAV platform.