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]本数据集隶属于欧盟H2020计划项目ICAERUS，专门聚焦于畜牧监测应用场景。更多项目详情可访问项目官网：https://icaerus.eu. 研究目标 对饲养数百只牲畜的牧场主而言，清点羊群与山羊群是一项极具挑战性的工作。本研究旨在开发一种基于计算机视觉（computer vision）的方法，用于统计通过通道或闸门的绵羊与山羊数量，该方法依托无人机拍摄的低空航拍视频（飞行高度低于15米）实现。 研究进展与优化 目前正在推进的工作包括： 无人机视频与标注： 已标注的绵羊过闸视频（DOI：10.5281/zenodo.12094356）。 模型开发： 用于绵羊检测的初始模型已开源至GitHub：https://github.com/ICAERUS-EU/UC3_Livestock_Monitoring。 为优化YOLO等检测模型，本团队正通过以下方式丰富数据集： 非白色小型反刍动物图像：当前模型因训练数据中白色绵羊占比高、非白色个体样本稀缺，难以准确识别非白色绵羊。为此，我们将纳入棕色及深色山羊的图像，以提升模型检测性能。 环境多样性扩充：正在收集不同环境条件下的额外图像与视频： - 拍摄背景：混凝土、沥青、草地、泥土等； - 光照条件：阴天、晴天及阴影区域（如畜棚阴影）。 数据集说明 本数据集为原始无标注图像与视频数据集的子集，现已补充山羊标注。由于未对小型反刍动物做品类区分，所有标注均统一标记为“绵羊”。 可于原始数据集获取更多图像与视频资源： Lebreton, A., Depuille, L., NICOLAS, E., & Helary, L. (2025). 用于计算机视觉的山羊航拍视频与图像[数据集]. Zenodo. https://doi.org/10.5281/zenodo.14591324 本数据集包含以下内容： Pradel：包含法国阿尔代什省某山羊养殖场5次飞行任务采集的图像与视频的目录。 飞行任务目录 图像：从5段视频中提取的图像（共287张），原始提取帧率为5帧/秒，仅保留了图像方差较低的部分。 Fllight_directory_name.zip：以YOLO格式标注山羊的压缩目录（包含2790个标记为"sheep"的边界框）。由于未对小型反刍动物做品类区分，所有边界框均统一标记为“绵羊”。 注意事项 在“CUT_oblique_DJI_20240522173449_0002_V.mp4”目录中，大量山羊位于阴影区域。在标准视觉模式下，这些个体几乎难以辨识，但通过调整对比度与亮度可提升辨识度。使用者可自行决定是否保留该部分标注，以避免在常规场景下引入过多噪声。 后续工作 本团队正持续对原始图像进行标注，并计划在完成后公开相关资源。本次优化旨在提升不同场景下小型反刍动物的检测精度，敬请期待后续发布的小型反刍动物原始图像数据集及新增标注资源。 合作与联系 本团队欢迎围绕该主题开展合作。若有咨询或进一步信息需求，请联系：Adrien Lebreton 邮箱：[e-mail]