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Automated detection of European wild mammal species in camera trap images with an existing and pre-trained computer vision model


The use of camera traps is a nonintrusive monitoring method to obtain valuable information about the appearance and behavior of wild animals. However, each study generates thousands of pictures and extracting information remains mostly an expensive, time-consuming manual task. Nevertheless, image recognition and analyzing technologies combined with machine learning algorithms, particularly deep learning models, improve and speed up the analysis process. Therefore, we tested the usability of a pre-trained deep learning model available on the TensorFlow hub–FasterRCNN+InceptionResNet V2 network applied to images of ten different European wild mammal species such as wild boar (Sus scrofa), roe deer (Capreolus capreolus), or red fox (Vulpes vulpes) in color as well as black and white infrared images. We found that the detection rate of the correct region of interest (region of the animal) was 94%. The classification accuracy was 71% for the correct species’ name as mammals and 93% for the correct species or higher taxonomic ranks such as “carnivore” as order. In 7% of cases, the classification was incorrect as the wrong species’ name was classified. In this technical note, we have shown the potential of an existing and pre-trained image classification model for wildlife animal detection, classification, and analysis. A specific training of the model on European wild mammal species could further increase the detection and classification accuracy of the models. Analysis of camera trap images could thus become considerably faster, less expensive, and more efficient.

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We would like to thank the developer of the open-source Tensorflow models and the team of the Open Images Dataset V4 as well as the Hainich National Park for providing camera trap images.


This research was supported by the University of Applied Sciences Erfurt (FHE).

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Conceptualization: Christin Carl, Ingolf ProfftMethodology: Christin CarlResource images: Alisa KlammFormal analysis and investigation: Christin CarlWriting—original draft preparation: Christin CarlWriting—review and editing: Fiona Schönfeld, Dirk Landgraf, Ingolf Profft, Alisa KlammFunding acquisition: Christin Carl, Dirk LandgrafSupervision: Fiona Schönfeld, Dirk Landgraf

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Correspondence to Christin Carl.

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Carl, C., Schönfeld, F., Profft, I. et al. Automated detection of European wild mammal species in camera trap images with an existing and pre-trained computer vision model. Eur J Wildl Res 66, 62 (2020).

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  • Computer vision
  • Image analysis
  • Camera trap
  • Pre-trained model
  • Wild mammal species