Material Classification with Active Thermography on Multiple Household Objects

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.