  Camera traps are used worldwide to monitor wildlife. Despite the increasing
availability of Deep Learning (DL) models, the effective usage of this
technology to support wildlife monitoring is limited. This is mainly due to the
complexity of DL technology and high computing requirements. This paper
presents the implementation of the light-weight and state-of-the-art YOLOv5
architecture for automated labeling of camera trap images of mammals in the
Bialowieza Forest (BF), Poland. The camera trapping data were organized and
harmonized using TRAPPER software, an open source application for managing
large-scale wildlife monitoring projects. The proposed image recognition
pipeline achieved an average accuracy of 85% F1-score in the identification of
the 12 most commonly occurring medium-size and large mammal species in BF using
a limited set of training and testing data (a total 2659 images with animals).
  Based on the preliminary results, we concluded that the YOLOv5 object
detection and classification model is a promising light-weight DL solution
after the adoption of transfer learning technique. It can be efficiently
plugged in via an API into existing web-based camera trapping data processing
platforms such as e.g. TRAPPER system. Since TRAPPER is already used to manage
and classify (manually) camera trapping datasets by many research groups in
Europe, the implementation of AI-based automated species classification may
significantly speed up the data processing workflow and thus better support
data-driven wildlife monitoring and conservation. Moreover, YOLOv5 developers
perform better performance on edge devices which may open a new chapter in
animal population monitoring in real time directly from camera trap devices.
