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Recent advancements in thermal imaging sensor technology have resulted in the use of thermal cameras in a variety of applications, including automotive, industrial, medical, defense and space, agriculture, and other related fields. Thermal imaging, unlike RGB imaging, does not rely on background light, and the technique is nonintrusive while also protecting privacy. This review article focuses on the most recent advancements in thermal imaging technology, key performance parameters, an overview of its applications, and machine-learning techniques applied to thermal images for various tasks. This article begins with the most recent advancements in thermal imaging, followed by a classification of thermal cameras and their key specifications, and finally a review of machine-learning techniques used on thermal images for various applications. This detailed review article is highly useful for designing thermal imaging-based applications using various machine-learning techniques.
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A. N. Wilson
University of North Florida
Khushi Gupta
Central University of Punjab
Balu Harshavardan Koduru
University of Agder
IEEE Sensors Journal
Columbia University
University at Buffalo, State University of New York
University of Agder
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Wilson et al. (Tue,) studied this question.
synapsesocial.com/papers/69db1a634a1e15904c836d1b — DOI: https://doi.org/10.1109/jsen.2023.3234335