Recognition of European mammals and birds in camera trap images using deep neural networks
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Philipps-Universität Marburg
Abstract
Most machine learning methods for animal recognition in camera trap images are limited
to mammal identification and group birds into a single class. Machine learning methods
for visually discriminating birds, in turn, cannot discriminate between mammals and are
not designed for camera trap images. The authors present deep neural network models to
recognise both mammals and bird species in camera trap images. They train neural
network models for species classification as well as for predicting the animal taxonomy,
that is, genus, family, order, group, and class names. Different neural network architec-
tures, including ResNet, EfficientNetV2, Vision Transformer, Swin Transformer, and
ConvNeXt, are compared for these tasks. Furthermore, the authors investigate ap-
proaches to overcome various challenges associated with camera trap image analysis. The
authors’ best species classification models achieve a mean average precision (mAP) of
97.91% on a validation data set and mAPs of 90.39% and 82.77% on test data sets
recorded in forests in Germany and Poland, respectively. Their best taxonomic classifi-
cation models reach a validation mAP of 97.18% and mAPs of 94.23% and 79.92% on
the two test data sets, respectively.
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Except where otherwised noted, this item's license is described as Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 - CC BY NC ND