Material classification with laser thermography and machine learning

Tamas Aujeszky, Georgios Korres, Mohamad Eid

Research output: Contribution to journalArticlepeer-review


Robotic devices that can perform teleoperation work in complex and unstructured environments (e.g. healthcare, education, automotive, telepresence, defence, etc.) rely mainly on visual and/or auditory feedback to interact with a remote operator. There is a vital need for measuring the physical properties of ambient environments when performing precise manipulation tasks over a distance, to improve the quality of performance. In this paper, we propose an approach to combine infrared thermography with machine learning for a fast, accurate way to classify objects of different material. A laser source stimulates the surface of the object while an infrared camera captures its thermal signature, extracts features about these signatures and feeds them into a machine learning-based algorithm for classification. Results demonstrated that a classification accuracy of around 97% can be achieved with majority vote Decision Tree classifier, even in the presence of data from multiple data acquisition sessions.

Original languageEnglish (US)
Pages (from-to)181-202
Number of pages22
JournalQuantitative InfraRed Thermography Journal
Issue number2
StatePublished - Apr 3 2019


  • Infrared imaging
  • haptic interfaces
  • image classification
  • machine learning
  • measurement by laser beam

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


Dive into the research topics of 'Material classification with laser thermography and machine learning'. Together they form a unique fingerprint.

Cite this