Emissivity is a crucial parameter for a quantitative thermography measurement. It influences measured temperature using an infrared camera. Typically, the emissivity is handled by infrared camera software but often for more complex tasks—e.g., setting and controlling the emissivity of individual pixels—a custom-made solution must be created. This can be especially beneficial for active thermography measurement and dynamic building inspection by infrared thermography as many surfaces of interest with different emissivity occur in thermographic data. In literature, one technique for suppressing emissivity occurs most often—the technique used by infrared camera manufacturers. Nonetheless, two other techniques are marginally mentioned. The most complex technique is the one used by infrared camera manufacturers, which allows many parameters to be set, but it is difficult to incorporate it into own solution. In contrast, the second one can be adapted easily, and it uses the relationship between emissivity and the fourth power of temperatures. The third one is a scarcely used technique that occurs for some active thermography measurements, in which a thermographic sequence in counts is divided by a frame when temperature equilibrium is reached. The main goal of this article is to compare these individual techniques from the point of view of the accuracy and possibility of use. The experiment showed that all three methods can be successfully used for the suppression of emissivity influence.

Industrial lasers are an expanding technology of welding and other materials processing. Lasers with optical scanning heads are often used, as these provide more versatility, accuracy, and speed. The output part of the scanning head is covered by a protective glass, which might get contaminated by various particles from the laser processing. This decreases the transmissivity of the glass, and it can affect the production quality. The contamination needs to be checked regularly, but a visual inspection might not always be effective. This paper proposes two alternative methods of inspecting the protective glass: flash-pulse active thermography, and laser active thermography. They are based on the thermal excitation of the glass and measuring the response with an infrared camera. The experimental setup and practical results are described and the advantages and disadvantages are discussed. The presented methods are proven to be effective in detecting the contamination of the glass.

Non-contact temperature measurement of persons during an epidemic is the most preferred measurement option because of the safety of personnel and minimal possibility of spreading infection. The use of infrared (IR) sensors to monitor building entrances for infected persons has seen a major boom between 2020 and 2022 due to the COVID-19 epidemic, but with questionable results. This article does not deal with the precise determination of the temperature of an individual person but focuses on the possibility of using infrared cameras for monitoring the health of the population. The aim is to use large amounts of infrared data from many locations to provide information to epidemiologists so they can have better information about potential outbreaks. This paper focuses on the long-term monitoring of the temperature of passing persons inside public buildings and the search for the most appropriate tools for this purpose and is intended as the first step towards creating a useful tool for epidemiologists. As a classical approach, the identification of persons based on their characteristic temperature values over time throughout the day is used. These results are compared with the results of a method using artificial intelligence (AI) to evaluate temperature from simultaneously acquired infrared images. The advantages and disadvantages of both methods are discussed.