Online Event “AI-powered Thermal Data Processing"
with technical lectures from thermography practice:

• Title: "AI in Thermography – How to Analyze Faster and Save Time?"
• Speaker: Dr. Krzysztof Dziarski, Poznan University of Technology
• Title: "AI-Driven Multimodal NDE Using Robot-Assisted Infrared Thermography"
• Speaker: Sruthi Krishna Kunji Purayil, Ph.D., Bundesanstalt für Materialprüfung, Berlin

Date: Tuesday, November 10, 2026
Time: 10:00 AM – 12:00 PM (CET)
Language: English
Price: Free of charge                                            

Register for the online event:
https://register.gotowebinar.com/register/6539476705391826777?source=PR-extern

Compared with methods such as pyrometry, infrared thermography provides not only individual temperature values but also complex thermograms and image sequences. These contain valuable thermal information from which conclusions can be drawn about the properties and quality of materials, components, and processes. Artificial intelligence can extract and analyze this information more effectively, recognize patterns, and reliably identify anomalies.

The webinar is intended, on the one hand, for thermography users who want to enhance their measurement methods with machine learning and deep learning. On the other hand, it is aimed at users of AI and machine vision systems who want to use thermal imaging data as an additional sensor input. Convolutional neural networks and predictive models offer a wide range of potential applications in this field.

Artificial intelligence already enhances several InfraTec thermography solutions. Applications include automatic ROI detection, reducing false alarms in early fire detection, and predicting hardness properties in hot forming. The webinar will examine these developments and show how thermal data can be integrated more effectively into future data-driven industrial applications. Two guest speakers will also present their own approaches to using AI to expand the capabilities of thermography.

A Glimpse at the Online Event       

• Use of convolutional neural networks and predictive models for thermal data processing
• Reliable AI models based on stable, reproducible, and high-quality input data from InfraTec infrared cameras
• Acquisition, analysis, processing, and export of thermographic measurement data with IRBIS® 3, including NumPy compatible formats
• Applications such as automatic ROI detection, fewer false alarms in early fire detection, and the prediction of hardness properties in hot forming
• Practical insights from two guest speakers on using AI to expand the capabilities of thermography