Title:
Material Classification with Active Thermography on Multiple Household Objects
Material Classification with Active Thermography on Multiple Household Objects
Author(s)
Chen, Haofeng
Advisor(s)
Kemp, Charles C.
Editor(s)
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Abstract
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.
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Date Issued
2019-05
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Resource Type
Text
Resource Subtype
Undergraduate Thesis