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.

翻译：本文提出了一种基于机器学习的方法，通过捕捉图像估计Pendubot角位置。首先引入一种基线算法，利用传统图像处理技术估计角度。该基线算法在Pendubot未快速运动的情况下表现良好。然而，当Pendubot因自由落体快速运动时，其在捕捉图像中呈现模糊物体，导致基线算法无法准确估计角度。为此，本文提出一种基于深度神经网络（DNN）的算法以应对这一挑战。该方法依托迁移学习理念，使DNN能在极小的微调数据集上完成训练。基线算法用于生成微调数据集的真实标注。在保留的评估集上的实验结果表明，所提方法在清晰图像和模糊图像上的中位绝对误差分别为0.02度和0.06度。