Résumé
1.Images are resourceful data for ecologists and can provide a more
complete information than other methods to study biodiversity and the
interactions between species. Automated image analysis however often
relies on extensive datasets, not implementable by small research teams.
We are here proposing an object detection method that allows the analysis
of high‐resolution images containing many animals interacting in a small
dataset. 2.We developed an image analysis pipeline named ‘CORIGAN' to
extract the characteristics of animal communities. CORIGAN is based on the
YOLOv3 model as the core of object detection. To illustrate potential
applications, we use images collected during a sentinel prey experiment.
3.Our pipeline can be used to detect, count and study the physical
interactions between various animals. On our example dataset, the model
reaches 86.6% precision and 88.9% recall at the species level or even at
the caste level for ants. The training set required fewer than 10 h of
labelling. Based on the pipeline output it was possible to build the
trophic and non‐trophic interactions network describing the studied
community. 4.CORIGAN relies on generic properties of the detected animals
and can be used for a wide range of studies and supports. Here, we study
invertebrates on high‐resolution images, but the same processing can be
transferred for the study of larger animals on satellite or aircraft
images.