(Georgia Institute of Technology, 2019-05)
Chen, Haofeng
Active thermography is a technique to inject heat into a target sample and observe the
temperature change along time. Such a technique enables a robot to perform material classification
with machine learning algorithms and infer material properties of its surroundings. We
present a study of material classification on 20 household objects of 5 material classes using
active thermography, and analyze factors that impact on material classifiers’ performance on
generalizing to heating distances and object instances not present during training. By performing
a 20-way classification of the object instances, we show that there is potential for classifiers
to generalize to unseen objects made from known material classes. The best-performing algorithm
trained on 15 object instances at 5 heating distances (20cm, 25cm, 30cm, 35cm, 40cm)
gives an accuracy of 71.7% when generalizing to 5 objects that are not in the training set.
(Georgia Institute of Technology, 2019-05)
Bai, Haoping
By briefly heating the local environment with a heat lamp and observing what happens with a thermal camera, robots could potentially infer properties of their surroundings. However, this form of active thermography introduces large signal variations compared to traditional active thermography, which has typically been used to characterize small regions of materials in carefully controlled settings. We demonstrate that a data-driven approach with modern machine learning methods can be used to classify material samples over relatively large surface areas and variable distances. We also introduce the use of z-normalization to improve material classification and reduce variation due to distance and heating intensity. Our best performing algorithm achieved an overall accuracy of 77.7% for multi-class classification among 12 materials placed at varying distances (20 cm, 30 cm, and 40 cm). The observations were made for 5 seconds with 1s of heating and 4s of cooling. We also provide a demonstration of performance with a multi-material scene.