构建真实牧场场景的牛脸识别数据集，推动畜牧智能化发展
ReCowGnition: A Realistic Biometric Benchmark for Cow Face Recognition
- 在真实奶牛场采集6838张图像，覆盖161头牛
- 设计6种评估协议，支持验证与识别任务对比研究
- 测试6类模型性能，涵盖小样本、跨物种和零样本方法
随着精准畜牧养殖和计算机视觉技术的发展，视觉动物生物特征识别受到关注。将已在人类中验证有效的生物识别技术应用于牲畜，可提升动物福利与生产效率。然而，复杂场景、外观相似、遮挡及非合作行为，以及公开标注数据集有限等问题仍存。本文提出一个全新的公开牛脸识别基准数据集，基于真实自动采集场景，包含6838张图像，涵盖161头不同奶牛。除公开数据集外，还定义了两个验证与四个识别评估协议，以促进该领域的可比性研究。此外，对六种基准模型在该数据集上的表现进行了评估，包括小样本训练模型、跨物种微调模型及零样本基础模型方法。
原文摘要 · Abstract (English)
With the development of precision livestock farming and the advances in computer vision, visual animal biometrics has gained attention. Using biometric technologies that have been proven effective for humans to identify livestock can increase animal welfare as well as production efficiency. However, challenges such as complex scenarios, similar appearances, occlusions, and non-cooperative behavior, as well as the limited amount of publicly available labeled datasets, remain. In this work, we contribute a novel, publicly available cow face benchmark dataset that has been collected in a realistic automatic scenario with 6,838 images of 161 different cows at a dairy farm. In addition to the public dataset, we define two verification and four identification evaluation protocols to foster comparable research in the cow recognition research field. Further, we provide evaluation results on our dataset of six benchmark models, which include models trained on limited data, cross-species fine-tuned models, and zero-shot foundation model approaches.
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