Recent Advances in Infrared Thermography and Sensing Technologies
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: 20 October 2025 | Viewed by 708
Special Issue Editors
Interests: deep learning; machine learning; infrared thermography; neural networks and artificial intelligence
Interests: infrared thermography; temperature measurement; deep learning; vision-based measurement; object detection; information fusion
Special Issues, Collections and Topics in MDPI journals
Interests: AI-powered automation for non-destructive testing; nighttime pedestrian detection; automated biomedical imaging recognition; 3D tomography for complex structures; reliability evaluation of non-destructive testing systems
Special Issues, Collections and Topics in MDPI journals
Interests: infrared thermography; image processing; automation; deep learning; machine learning; defect detection; infrastructure conservation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Infrared thermography (IRT) has emerged as a cutting-edge, non-contact, and non-destructive technology, offering significant potential in two major areas: defect detection and temperature measurement. The rapid progress of artificial intelligence (AI) and deep learning methods has revitalized IRT, enhancing its performance and expanding its application across various industries and research fields.
In industrial production, defect detection and temperature measurement are crucial for maintaining product quality and ensuring process efficiency. Infrared thermography, with its ability to capture detailed thermal profiles without direct contact, has found extensive use in sectors such as electricity, metallurgy, aerospace, and medicine. However, challenges remain in optimizing IRT for these applications, particularly in integrating it with advanced computational tools.
- Defect Detection with Infrared Thermography:
Combining IRT with machine learning techniques holds great promise for improving detection accuracy and reliability, especially in complex environments. Key research directions include the extraction of meaningful thermal features, the reduction of noise, and the development of AI-driven models tailored to specific materials and processes. - Temperature Measurement with Infrared Thermography:
Temperature measurement with IRT can be influenced by several factors, including ambient temperature, distance, and external interferences such as dust and water mist. Overcoming these challenges to ensure accurate and reliable temperature measurement is critical for expanding IRT's industrial applications, particularly in high-precision scenarios.
We invite you to submit original research articles and review papers that focus on the latest advancements in infrared thermography, particularly in the integration of AI and machine learning for defect detection and temperature measurement. Contributions that address the challenges of real-world industrial applications are highly encouraged.
Topics of Interest
We welcome submissions on the following topics:
- Innovations in infrared thermography for defect detection and temperature measurement;
- The integration of artificial intelligence and machine learning with infrared thermography for industrial use;
- Non-destructive evaluation (NDE) techniques based on infrared thermography;
- Solutions for mitigating environmental impacts, such as dust, water mist, and measurement distance, on IRT performance;
- Application case studies of IRT in electricity, metallurgy, medicine, and other sectors.
We look forward to your contributions to further advancing the use of infrared thermography and sensing technologies in industry and research.
Dr. Qiang Fang
Guest Editor Assistant
Dr. Dong Pan
Dr. Yuxia Duan
Dr. Iván Garrido González
Guest Editors
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Keywords
- infrared thermography
- defect detection
- temperature measurement
- non-destructive evaluation (NDE)
- artificial intelligence
- machine learning
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