Improving the domain adaptation of camera trap image classifiers using inserted animal cutouts

Abstract

Camera trap studies are increasingly utilising deep neural network (DNNs) models to automate animal classification. Data collected in this domain are dominated by few species and locations, causing models to overfit to training locations whilst limiting applicability to new areas. To mitigate this, we extract animal cutouts and insert them into empty camera trap images to synthetically augment image variation. We employ a factorial design to determine the optimal composition of DNN training data by manipulating the distributions of one or more potential sources of overfitting: imbalanced animal classes, day vs. night photos, and camera trap locations. We demonstrate that DNN generalisation and performance in new real-world locations is significantly improved through incorporating synthetic images with diverse but equally represented locations in training data, but imbalanced classes. We provide analytic insights for other studies looking to use this technique to reduce model dependence on background features to classify animal species.Ontario Ministry of Natural Resource