论文标题
转移学习，用于使用深层神经网络估算Pendubot角位置
Transfer Learning for Estimation of Pendubot Angular Position Using Deep Neural Networks
论文作者
论文摘要
在本文中，引入了基于机器学习的方法，以从其捕获的图像中估算Pendubot角位置。最初，引入了基线算法，以使用常规图像处理技术估算角度。对于Pendubot不快速移动的情况，基线算法表现良好。但是，当由于自由下落而迅速移动时，pendubot以基线算法无法估算角度的方式出现在捕获的图像中的模糊对象。因此，引入了基于深神经网络（DNN）算法以应对这一挑战。该方法依赖于转移学习的概念，以便在非常小的微调数据集中对DNN进行培训。基本算法用于创建微型数据集的地面真实标签。持有评估集的实验结果表明，提出的方法分别为锋利和模糊图像实现了0.02和0.06度的中值误差。
In this paper, a machine learning based approach is introduced to estimate pendubot angular position from its captured images. Initially, a baseline algorithm is introduced to estimate the angle using conventional image processing techniques. The baseline algorithm performs well for the cases that the pendubot is not moving fast. However, when moving quickly due to a free fall, the pendubot appears as a blurred object in the captured image in a way that the baseline algorithm fails to estimate the angle. Consequently, a Deep Neural Network (DNN) based algorithm is introduced to cope with this challenge. The approach relies on the concept of transfer learning to allow the training of the DNN on a very small fine-tuning dataset. The base algorithm is used to create the ground truth labels of the fine-tuning dataset. Experimental results on the held-out evaluation set show that the proposed approach achieves a median absolute error of 0.02 and 0.06 degrees for the sharp and blurry images respectively.
