Transfer Learning for Estimation of Pendubot Angular Position Using Deep Neural Networks

TL;DR

This paper presents a transfer learning-based deep neural network approach to accurately estimate the angular position of a Pendubot from images, effectively handling motion blur where traditional methods fail.

Contribution

It introduces a novel transfer learning method for pendubot angle estimation from images, especially under motion blur conditions, using a small fine-tuning dataset.

Findings

Median absolute error of 0.02 degrees for sharp images

Median absolute error of 0.06 degrees for blurry images

Effective estimation despite motion blur

Abstract

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.