DOI: 10.5281/zenodo.14967218
title: Drone videos and images of sheep in various conditions (for computer vision purpose)

description: 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).

Annotated images of goats/small ruminants in various conditions (https://doi.org/10.5281/zenodo.14929693 )


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 (of different colors), dirt, etc.

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



This dataset encompasses the following data:

-        Carmejane: a directory encompassing images and videos from a sheep farm in France, Alpes de Haute Provence.

         -        Videos: a directory encompassing short drone videos (22 videos ; Drone height: ~ 15 m ; Drone gimbal angle: NADIR and Oblique, Resolution: 3840x2160, FPS: 30 & 60, mostly white sheep, large variety of backgrounds). The videos are orignal or were cut.

         -        Images_from_videos: images extracted from the videos at 1 image per seconde (1014 images)

         -        Other_images: other images (165 images ;Drone height: ~ 15m ; Drone gimbal angle: NADIR & Oblique; Various Resolution)

-        Mourier: a directory encompassing images and videos from a sheep farm in France, Limousin.

         -        Videos: a directory encompassing short drone videos (6 videos ; Drone height: ~ 15-30 m ; Drone gimbal angle: Oblique, Resolution: 3840x2160, FPS: 30, white sheep, various backgrounds). The videos were cut.

         -        Images_from_videos: images extracted from the videos at 1 images per seconde (110 images)

         -        Other_images: other images (26 images ;Drone height: ~ 0-30m ; Drone gimbal angle: NADIR & Oblique; Various Resolution)

 

-        Summaries of images and videos

Future Work

We are actively annotating the collected images and plan to share some of them upon completion. These enhancements aim to improve detection accuracy for small ruminants in diverse scenarios. New detection models will also be shared on our Github page in the next months.

Collaboration and Contact

We welcome collaborations on this topic. For inquiries or further information, please contact:Adrien LebretonEmail: adrien.lebreton@idele.fr