## 3 Results and Analysis of Radiometric Thermal Data
### Basic Calibration When Using Several Different Cameras  
Infrared thermal-imaging cameras represent a significant financial item in the overall project cost, and their price increases with resolution. Although the cost of a single pixel decreases [42], as seen in Figure 19, as resolution increases, the number of pixels increases, and so does the total cost of the camera sensor. To optimize the final product, it is planned to install a thermal-imaging camera, which is the optimum in terms of complexity of the space where the FireBot will patrol. The main technical parameters are field of view (FOV), spatial resolution (IFOV), thermal sensitivity/NETD, and accuracy in (\({}^{\circ}\)C) or (\(\%\)) of the reading. Comparing the data from Figure 18 to the typical camera-accuracy value of \(\pm\)2 \({}^{\circ}\)C or \(\pm\)2\(\%\), we can see that the camera accuracy significantly affects the radiometric data. Figure 18: Polar diagram of directional spectral emissivity of the object and influence of emissivity changes on the temperature displayed by the thermal camera Flir E60 bx. The choice of camera affects the behavior of the system in which it is located. When developing an algorithm for temperature-anomaly detection using an IRT camera and the radiometric data provided by the camera, we consider that the data (temperatures) are correct or within the range of manufacturer-defined error. To ensure that these data are correct, all IRT cameras must be calibrated. Calibration is a process in which the infrared radiation that a camera detects is correlated with known temperatures. All cameras on the market are calibrated to factory specifications, but over time and due to the aging of electronics, a calibration shift is caused, and consequently, cameras produce inaccurate temperature measurements. Unfortunately, the owner of the camera cannot recalibrate the camera on their own but can determine the camera's deviation for a certain measurement point with the help of a body with a known temperature. For that purpose, a blackbody source is used. A blackbody is a physical body with high emissivity, which means that it radiates and absorbs almost all electromagnetic radiation. The blackbody has a predefined range of temperatures it can achieve and, as such, can be used as a reference point for determining the camera's accuracy. When talking about the manufacturer's calibration, the correction table is directly saved in the camera's firmware and automatically corrects camera readings, whereas in our case, the correction table was used to manually (or within our algorithm) correct detected temperatures. In this subsection, we used seven IRT cameras, presented in Table 3, and two blackbodies, presented in Table 4, to determine whether their accuracy was within the manufacturer's margin of error and whether they can be used as such on FireBot for fast and efficient temperature-anomaly detection. All seven cameras were calibrated at seven temperature points, ranging from 27 \({}^{\circ}\)C to 45 \({}^{\circ}\)C with a 3 \({}^{\circ}\)C step in between. This range was chosen because it represents the temperature interval in the range of increased latent hazards, such as passive consumption of electrical equipment when overheated and triggering fire hazards. Increasingly higher temperature values automatically become an area of interest and further analysis due to their height and temperature difference relative to the environment. All measurements were repeated 10 times and the results were expressed as average values. For this reason, the values in the tables are expressed in two decimal places. It is important to note that raw camera data can be downloaded to three decimal places. Figure 19: Single-sensor pixel-cost evolution, source [42]. It is difficult to compare the characteristics of the cameras listed in the Table 3 from the point of view of image analysis if the parameters FOI and IFOV are not taken into account.