# Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics
## Appendix 1```
The coefficients \(\beta\), \(\epsilon\), \(\delta\), and \(\theta\) emerge as critical factors influencing the performance of victim detection algorithms, suggesting the need for the careful consideration and optimization of these parameters in the designed system. On the other hand, the evaluation of processing time, measured in frames per second (fps) for the three cases using the YOLOv8-m version, demonstrates a performance ranging from approximately 26 to 28 fps for inference through the convolutional neural network (CNN) on thermal and RGB images, respectively. Meanwhile, the multispectral range exhibits a rate of 8 fps, attributable to the preprocessing and size of images across different spectral ranges. Although the processing times in the first two cases approach so-called real-time processing, the last case is an exception. To compensate for this latency, a pause sequence is executed, during which the robot remains stationary in the sample acquisition zone to gather and process data, effectively overlaying this time latency. Although individual victim detection systems tend to perform well in specific cases, the results demonstrate that a system cannot be generalized for all scenarios. Therefore, once the individual impact of each coefficient is known, it is necessary to establish a robust and redundant system for victim detection. Figure 10 illustrates the maximized outcome of the areas covered by the implemented redundant system, reaching a success rate of up to 93.5% (following Table 4) in potential search and rescue scenarios. #### 4.2.4 Discussion

The approach to victim detection, and the development of a substantial comparison among the three types of images to achieve this purpose, has allowed the establishment of criteria for examining the strengths and weaknesses for each image type under specific functionality conditions. While, individually, the systems exhibit acceptable functionality in particular conditions, the proposed synergy and selective use of image types based on environmental conditions result in a detection accuracy exceeding 93%, as observed in the conducted experiments. In this context, the optimal functionality conditions for the different sensors are as follows: thermal sensors perform well under changing light conditions, detect individuals regardless of clothing colour, and effectively identify partially or fully covered victims in low-light conditions. Multispectral sensors demonstrate strength in scenarios involving heat sources in summer and fire conditions, both indoors and outdoors. Finally, RGB sensors excel in processing time. Figure 10: Approach to the maximized area, according to the best values of the proposed coefficients in Figure 8. Source: authors. Regarding the state of the art, no similar work has been found that compares the three image types by evaluating environment metrics and functionality; most approaches to victim detection in search and rescue environments are carried out with thermal cameras due to the applicability of computer vision techniques for the segmentation of the thermal information of a victim and their environment, in addition to the commercial availability of equipment oriented to this task. However, there are significant advances in using multispectral perception cameras to detect biometric characteristics. This may be useful for feature extraction oriented to victim detection in the non-visible spectrum for post-disaster environments. Likewise, almost all works focus on integrating sensors in UAVs from the top view plane, working on datasets. Along these lines, references [22; 38] propose a different approach based on detecting and localizing victims online from UGVs in navigation areas with low-light conditions. Table 1 provides a detailed overview of the most relevant works and presents a comparison concerning the methods and techniques employed in their development.