Aerial videos and images of goats (for computer vision purpose)

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 ? Number 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 encompasses the following data: Pradel: a directory encompassing images and videos from a goat farm in France, Ardeche. Videos: a directory encompassing short drone videos (8 videos ; Drone height: ~ 15 m ; Drone gimbal angle: NADIR and Oblique, Resolution: 3840x2160, FPS: 30, brown goats, various backgrounds). The videos are orignal or were cut. Images_from_videos: images extracted from the videos at 5 images per seconde (1606 images) Other_images: other images (8 of images ;Drone height: ~ 15m ; Drone gimbal angle: NADIR Resolution: 5280x3956) Ferme nord: a directory encompassing images and videos from a goat farm in France, nord. Videos: a directory encompassing short drone videos (2 videos ; Drone height: ~ 15-30 m ; Drone gimbal angle: Oblique, Resolution: 3840x2160, FPS: 30, Dark brown goats, grass backgrounds). The videos were cut. Images_from_videos: images extracted from the videos at 5 images per seconde (442 images) Summaries of images and videos Future Work 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. Collaboration and Contact We welcome collaborations on this topic. For inquiries or further information, please contact:Adrien LebretonEmail: [e-mail]本数据集依托欧盟地平线2020（Horizon 2020）项目ICAERUS开发，专为畜牧监测应用场景打造。本研究旨在探索基于无人机（drone）的计算机视觉方法在真实农业环境中监测小型反刍动物的应用潜力。 更多项目信息可访问项目官网：https://icaerus.eu # 研究目标 对饲养数百只牲畜的牧场主而言，清点绵羊和山羊的数量是一项极具挑战性的日常工作。传统清点方式耗时久且易出现差错。 本研究旨在开发一种基于计算机视觉的方法，能够在牲畜通过走廊、闸门或其他自然受限通道时，自动检测、追踪并计数绵羊与山羊。 所提方案依托无人机采集的低空航拍视频（飞行高度低于15米），提供顶视视角，便于对牲畜进行检测与计数。 # 研究进展与优化 本工作包含针对绵羊低空航拍图像（飞行高度<15米）的专用数据集与模型开发。 ## 数据集贡献 本研究在2023-2026年间构建并丰富了多组带标注或无标注的数据集（详见汇总表）。 | 数据集名称 | 版本 | 发布日期 | 链接 | 引用方式 | 图像数量 | 视频数量 | 边界框数量 | | --- | --- | --- | --- | --- | --- | --- | --- | | 法国放牧区域牛群无人机原始图像 | v1 | 2023-08-10 | 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 | - | - | | 放牧奶牛无人机图像及标注 | v1 | 2023-12-01 | 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 | - | - | | 放牧奶牛无人机图像及标注 | v2 | 2024-04-01 | 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 | | 低空无人机拍摄的绵羊视频 | v1 | 2023-12-18 | 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 | - | | 用于计数的过路绵羊无人机视频及标注 | v1 | 2024-06-18 | 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 | | 用于计算机视觉研究的山羊航拍视频与图像 | v1 | 2025-01-03 | 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 | - | | 用于计算机视觉研究的山羊/小型反刍动物无人机图像及标注 | v1 | 2025-02-26 | 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 | | 多种场景下的绵羊无人机视频与图像（用于计算机视觉研究） | v1 | 2025-03-04 | 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 | - | | 多种场景下的绵羊无人机视频与图像（用于计算机视觉研究）——第二部分 | v1 | 2026-03-06 | 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 | - | | 多种场景下的绵羊无人机图像及标注（用于计算机视觉研究） | v1 | 2026-03-06 | 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 | | 用于测试绵羊计数计算机视觉管线的无人机视频 | v1 | 2026-03-06 | 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 | - | | **总计** | - | - | - | - | 9531 | 203 | 40114 | ## 模型开发 我们开发了用于小型反刍动物检测、追踪与计数的计算机视觉模型（v4版本的mAP@0.5达到0.99）。相关模型与代码已开源至GitHub：https://github.com/ICAERUS-EU/UC3_Livestock_Monitoring 为提升YOLO等检测模型的性能与鲁棒性，我们对数据集进行了丰富，以增加以下维度的多样性： 1. 环境条件（背景类型与光照条件） 2. 牲畜外观，包括现有数据集中常被低估的非白色绵羊与山羊。 # 数据集详情 本数据集包含以下内容： 1. **Pradel目录**：包含法国阿尔代什省某山羊养殖场的图像与视频。 - **Videos**：包含多段短时长无人机航拍视频（共8段；飞行高度约15米；无人机云台角度为天顶视角（NADIR）与斜视角；分辨率3840×2160，帧率30 FPS；拍摄对象为棕色山羊，背景多样）。视频为原始素材或经剪辑所得。 - **Images_from_videos**：从视频中以每秒5帧的速率提取的图像（共1606张）。 - **Other_images**：其他图像（共8张；飞行高度约15米；无人机云台角度为天顶视角；分辨率5280×3956）。 2. **Ferme nord目录**：包含法国北部某山羊养殖场的图像与视频。 - **Videos**：包含多段短时长无人机航拍视频（共2段；飞行高度约15-30米；无人机云台角度为斜视角；分辨率3840×2160，帧率30 FPS；拍摄对象为深棕色山羊，背景为草地）。视频均经剪辑处理。 - **Images_from_videos**：从视频中以每秒5帧的速率提取的图像（共442张）。 # 未来工作 在完成大量数据采集与标注工作后，我们的下一目标是完成绵羊计数管线的开发，并将其部署至边缘计算解决方案中，以实现在实际牧场环境中开展实时畜牧监测。 与此同时，另有多个项目正在探索计算机视觉在绵羊养殖中的其他应用场景，进一步拓展该技术的应用潜力。 # 合作与联系方式 我们欢迎围绕该主题开展合作。如有咨询或进一步信息需求，请联系： Adrien Lebreton 邮箱：[e-mail]