Ramesh Manu, Reibman Amy R
School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.
Sensors (Basel). 2024 Nov 30;24(23):7680. doi: 10.3390/s24237680.
We propose a self-training scheme, SURABHI, that trains deep-learning keypoint detection models on machine-annotated instances, together with the methodology to generate those instances. SURABHI aims to improve the keypoint detection accuracy not by altering the structure of a deep-learning-based keypoint detector model but by generating highly effective training instances. The machine-annotated instances used in SURABHI are hard instances-instances that require a rectifier to correct the keypoints misplaced by the keypoint detection model. We engineer this scheme for the task of predicting keypoints of cattle from the top, in conjunction with our Eidetic Cattle Recognition System, which is dependent on accurate prediction of keypoints for predicting the correct cow ID. We show that the final cow ID prediction accuracy on previously unseen cows also improves significantly after applying SURABHI to a deep-learning detection model with high capacity, especially when available training data are minimal. SURABHI helps us achieve a top-6 cow recognition accuracy of 91.89% on a dataset of cow videos. Using SURABHI on this dataset also improves the number of cow instances with correct identification by 22% over the baseline result from fully supervised training.
我们提出了一种自训练方案SURABHI，该方案利用机器标注的实例训练深度学习关键点检测模型，并介绍了生成这些实例的方法。SURABHI旨在提高关键点检测精度，不是通过改变基于深度学习的关键点检测器模型的结构，而是通过生成高效的训练实例。SURABHI中使用的机器标注实例是困难实例，即需要校正器来纠正被关键点检测模型误放置的关键点的实例。我们结合我们的超分辨率牛识别系统，为从顶部预测牛的关键点这一任务设计了该方案，该系统依赖于关键点的准确预测来预测正确的牛ID。我们表明，将SURABHI应用于高容量的深度学习检测模型后，对之前未见过的牛的最终牛ID预测准确率也显著提高，尤其是在可用训练数据很少的情况下。SURABHI帮助我们在一个牛视频数据集上实现了91.89%的前6名牛识别准确率。在这个数据集上使用SURABHI，与完全监督训练的基线结果相比，正确识别的牛实例数量也增加了22%。
