CattleFace-RGBT：RGB-T牛面部关键点基准
CattleFace-RGBT: RGB-T Cattle Facial Landmark Benchmark
AI总结：
本文提出首个RGB-T牛面部关键点数据集CattleFace-RGBT，通过半自动AI辅助标注迁移训练模型，实现RGB与热成像图像面部关键点定位，为未来研究建立基准。
摘要：
为应对这一挑战，我们引入了CattleFace-RGBT，这是一个RGB-T牛面部关键点数据集，包含2,300对RGB-T图像，共计4,600张图像。创建关键点数据集非常耗时，但AI辅助标注可以提供帮助。然而，由于直接热成像训练效果不佳，以及不同相机视角导致RGB-热成像对齐不可行，将AI应用于热成像图像具有挑战性。因此，我们选择将在RGB上训练的模型迁移到热成像图像上，并使用我们的AI辅助标注工具按照半自动标注方法进行优化。在RGB和热成像图像上准确定位面部关键点，使我们不仅能识别牛的呼吸迹象，还能测量温度以评估动物的热状态。据我们所知，这是首个用于RGB-T图像上牛面部关键点的数据集。我们在各种主干架构上对CattleFace-RGBT数据集进行了基准测试，旨在为未来的研究、分析和比较建立基线。数据集和模型位于https://github.com/UARK-AICV/CattleFace-RGBT-benchmark
英文摘要：
To address this challenge, we introduce CattleFace-RGBT, a RGB-T Cattle Facial Landmark dataset consisting of 2,300 RGB-T image pairs, a total of 4,600 images. Creating a landmark dataset is time-consuming, but AI-assisted annotation can help. However, applying AI to thermal images is challenging due to suboptimal results from direct thermal training and infeasible RGB-thermal alignment due to different camera views. Therefore, we opt to transfer models trained on RGB to thermal images and refine them using our AI-assisted annotation tool following a semi-automatic annotation approach. Accurately localizing facial key points on both RGB and thermal images enables us to not only discern the cattle's respiratory signs but also measure temperatures to assess the animal's thermal state. To the best of our knowledge, this is the first dataset for the cattle facial landmark on RGB-T images. We conduct benchmarking of the CattleFace-RGBT dataset across various backbone architectures, with the objective of establishing baselines for future research, analysis, and comparison. The dataset and models are at https://github.com/UARK-AICV/CattleFace-RGBT-benchmark
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
