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This dataset was developed within the framework of the European Horizon 2020 project ICAERUS, specifically for the livestock monitoring use case. The objective of this work is to explore the potential of drone-based computer vision methods for monitoring small ruminants in real farming environments. More information about the project is available on the project website: https://icaerus.eu Objective Counting sheep and goats is a significant operational challenge for farmers managing flocks that may contain hundreds of animals. Traditional counting methods are time-consuming and prone to errors. The objective of this work is to develop a computer vision–based methodology capable of automatically detecting, tracking, and counting sheep and goats when animals pass through a corridor, gate, or other naturally constrained passage. The proposed approach relies on low-altitude aerial videos (<15 m) acquired using drones, providing a top-down perspective that facilitates the detection and counting of animals. Progress and Enhancements Our work includes the development of datasets and models dedicated to low-altitude aerial imagery of sheep (<15 m). Datasets contributions: Multiple datasets either with or without annotations, have been produced and enriched as part of this work during the 2023-2026 period (see the summary table). Name Version Date Link How to quote ? Numebr of Images Number of Videos Number of Bounding Boxes Drone raw images of cattle in french grazing areas v1 10-08-2023 https://zenodo.org/records/8234156 Lebreton, A. (2023). Drone raw images of cattle in french grazing areas [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8234156 900 Drone images and their annotations of grazing cows v1 01-12-2023 https://zenodo.org/records/10245396 Lebreton, A., & Helary, L. (2023). Drone images and their annotations of grazing cows [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10245396 1100 Drone images and their annotations of grazing cows v2 01-04-2024 https://zenodo.org/records/11048412 Helary, L., & Lebreton, A. (2024). Drone images and their annotations of grazing cows [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11048412 1385 4941 Sheep videos taken from drone at low altitude v1 18-12-2023 https://zenodo.org/records/10400302 Lebreton, A., & Helary, L. (2023). Sheep videos taken from drone at low altitude [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10400302 16 Drone videos and their annotations of passing sheep (for counting purpose) v1 18-06-2024 https://zenodo.org/records/12094356 Helary, L., Okoye, K. N., Kolodziejczyk, M., Schewe, J., Philip, L., Nicolas, E., & Lebreton, A. (2024). Drone videos and their annotations of passing sheep (for counting purpose) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.12094356 4 14365 Aerial videos and images of goats (for computer vision purpose) v1 03-01-2025 https://zenodo.org/records/14591324 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 2056 10 Drone images and their annotations of goats/small ruminants (for computer vision purpose) v1 26-02-2025 https://zenodo.org/records/14929694 Lebreton, A., Duval, L., Depuille, L., Nicolas, E., & Helary, L. (2025). Drone images and their annotations of goats/small ruminants (for computer vision purpose) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14929694 287 2790 Drone videos and images of sheep in various conditions (for computer vision purpose) v1 04-03-2025 https://zenodo.org/records/14967219 Lebreton, A., Morin, C., Nicolas, E., & Helary, L. (2025). Drone videos and images of sheep in various conditions (for computer vision purpose) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14967219 1315 28 Drone videos and images of sheep in various conditions (for computer vision purpose) - Part II v1 06-03-2026 https://zenodo.org/records/18889354 Lebreton, A., Helary, L., NICOLAS, E., Goin, L., Grisot, P.-G., & Jegorel, T. (2026). Drone videos and images of sheep in various conditions (for computer vision purpose) - Part II [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18889354 1679 47 Drone images and their annotations of sheep in various conditions (for computer vision purpose) v1 06-03-2026 https://zenodo.org/records/18889623 Lebreton, A., de Brito, A., Blaise, E., Jegorel, T., Goin, L., Grisot, P.-G., NICOLAS, E., & Helary, L. (2026). Drone images and their annotations of sheep in various conditions (for computer vision purpose) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18889623 809 18018 Drone videos to test sheep counting computer vision counting pipeline v1 06-03-2026 https://zenodo.org/records/18889878 Lebreton, A., Grisot, P.-G., Depuille, L., Goin, L., NICOLAS, E., & Helary, L. (2026). Drone videos to test sheep counting computer vision pipeline [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18889878 98 TOTAL 9531 203 40114 Model Development: We developed computer vision models for small ruminant detection (0.99 mAP50 in its version 4), tracking, and counting.The models and associated code are available on GitHub:https://github.com/ICAERUS-EU/UC3_Livestock_Monitoring To improve the performance and robustness of detection models such as YOLO, the datasets were enriched to increase variability in: Environmental conditions (background types and lighting conditions) Animal appearance, including non-white sheep and goats, which are often underrepresented in existing datasets. Data set description This dataset is linked to the dataset “Drone videos and images of sheep in various conditions (for computer vision purposes) – Part I & II.” While the previous datasets mainly provide raw drone imagery, this dataset focuses on annotated images, selected to increase the diversity and representativeness of the available annotations. All annotations are provided in the Ultralytics YOLO Detection 1.0 format, allowing direct use for training or evaluating object detection models. The dataset is organized into two main directories: all: contains all annotated images test: evaluation subsets designed for model benchmarking This dataset encompasses the following data: all a directory encompassing the full set of images and their annotations. images: 809 images with 769 containing sheep and thus labels labels: 18018 sheep bounding boxes test: evaluation subsets designed for model benchmarking (See more on the models on the Github repository) Nadir_and_oblique images: 479 images with 428 NADIR and 51 oblique labels: all the sheep bounding boxes linked to these images Nadir_only images : 428 images only from a NADIR point of a view labels : all the sheep bounding boxes linked to these images Future Work Part of these data are available with annotations in another dataset (see the table above). Following extensive efforts in data collection and annotation, our next objective is to finalize and deploy the sheep counting pipeline on an edge computing solution, enabling real-time livestock monitoring in operational farm environments. In parallel, additional projects are exploring other computer vision applications in sheep farming, expanding the potential use cases of this technology. Acknowledgments The authors would like to acknowledge Thomas Jegorel (EPLEFPA de Saint-Gaudens) and the project F.A.A.N., for the valuable contribution to this work. As an external contributor to the ICAERUS project, he generously provided access to a substantial amount of field data and facilitated the collection of drone imagery in operational and diverse farm conditions. The authors also thank Alexandra and Ewen for their significant efforts in data annotation, which were essential for the development of the dataset. Collaboration and Contact We welcome collaborations on this topic. For inquiries or further information, please contact:Adrien LebretonEmail: adrien.lebreton@idele.fr