Material classification with laser thermography and machine learning. Issue 2 (3rd April 2019)

• Record Type: Journal Article
• Authors: Aujeszky, Tamas; Korres, Georgios; Eid, Mohamad
• Is Part Of: Quantitative infrared thermography. Volume 16:Issue 2(2019)
• Page Start: 181
• Page End: 202
• Publication Date: 2019-04-03
• Subjects: Infrared imaging -- measurement by laser beam -- machine learning -- image classification -- haptic interfaces; Infrared technology -- Periodicals; Thermography -- Periodicals; Infrared technology; Thermography; Periodicals; 621.362
• DOI: 10.1080/17686733.2018.1539895
• Languages: English
• ISSNs: 1768-6733

Abstract

ABSTRACT: 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.