Automated location invariant animal detection in camera trap images using publicly available data sources

1. A time-consuming challenge faced by ecologists is the extraction of meaningful data from camera trap images to inform ecological management. Automated object detection solutions are increasingly, however, most are not sufficiently robust to be deployed on a large scale due to lack of location invariance across sites. This prevents optimal use of ecological data and results in significant resource expenditure to annotate and retrain object detectors.

2. In this study, we aimed to (a) assess the value of publicly available image datasets including FlickR and iNaturalist (FiN) when training deep learning models for camera trap object detection (b) develop a for training location invariant object detection models and (c) explore the use of small subsets of camera trap images for optimization training.

3. We collected and annotated 3 datasets of images of striped hyena, rhinoceros and pig, from FiN, and used transfer learning to train 3 object detection models in the task of animal ...