Drone images and their annotations of goats/small ruminants (for computer vision purpose)

This dataset is part of the European H2020 project ICAERUS, specifically focused on the livestock monitoring use case. For more information, visit the project website: https://icaerus.eu.

Objective

Counting sheep and goats is a significant challenge for farmers managing flocks with hundreds of animals. Our objective is to develop a computer vision-based methodology to count sheep and goats as they pass through a corridor or gate. This approach utilizes low-altitude aerial videos (<15 m) recorded by drones.

Progress and Enhancements

Our ongoing efforts include:

Drone Videos and Annotations:

Annotated videos of sheep passing through gates (DOI: 10.5281/zenodo.12094356).

Model Development:

Initial models for sheep detection, available on GitHub: https://github.com/ICAERUS-EU/UC3_Livestock_Monitoring.

To improve detection models like YOLO, we are enriching the dataset with:

Images of Non-White Small Ruminants: Current models struggle with detecting sheep that are not white due to their low frequency in flocks and thus datasets. By including images of brown and dark-colored goats, we aim to enhance model performance.

Environmental Diversity: Additional images and videos are being collected under varying conditions:

Backgrounds: Concrete, asphalt, grass, dirt, etc.

Lighting Conditions: Cloudy, sunny, and shaded (e.g., barn shadows).

Data set description

This dataset is a subset of an original dataset of images and videos without annotations, now enhanced with annotations of goats. The annotation is labeled as “sheep” since no distinction is made between small ruminants.

Find more images and videos in the original dataset:

 Lebreton, A., Depuille, L., NICOLAS, E., & Helary, L. (2025). Aerial videos and images of goats (for computer vision purpose) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14591324

This dataset encompasses the following data:

Pradel: a directory encompassing images and videos from a goat farm in France, Ardeche from 5 flights.

Flight directory

Images: images extracted from 5 videos (287 images), originally extracted at a frame of 5 frames/sec, when images variance was low, only some images remain.

Fllight_directory_name.zip:  a .zip directory with the annotations of goats at the YOLO format (2790 “sheep” bounding boxes). The bounding boxes are labeled as “sheep” since no distinction is made between small ruminants.

Warning

In the directory "CUT_oblique_DJI_20240522173449_0002_V.mp4", a large number of goats are located in a shaded area. In standard vision, they are barely discernible, but by adjusting contrast and brightness, they become more visible. Users are free to decide whether they want to keep these annotations or not, to avoid introducing too much noise under typical conditions.

Future Work

We are actively continuing annotations on raw images and plan to share them upon completion. These enhancements aim to improve detection accuracy for small ruminants in diverse scenarios. Stay tuned for new dataset of raw images of small ruminants and new available annotations.

Collaboration and Contact

We welcome collaborations on this topic. For inquiries or further information, please contact:Adrien LebretonEmail: [e-mail]