# Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics
## 1 Introduction  
The past decade has witnessed significant technological advancements in perception systems, with a particular focus on vision-based systems applied in the domain of field robotics. Notably, a considerable impetus has been observed in applications focused on vision sensors, as highlighted in [1; 2; 3]. These kinds of sensors leverage different light spectrum ranges to generate environmental data, thereby facilitating the extraction of valuable information essential for robotic mission applications. Sensors incorporated in RGB, thermal, and multispectral cameras are the most widely utilized in outdoor robotics, particularly the first two sensors, which are commonly applied in the Search and Rescue (SAR) domain [4; 5]. In contrast, the latter (multispectral) exhibits extensive applicability in precision agriculture [6; 7]; recent studies have demonstrated its utility in SAR robotics.  
Within the spectrum of applied advancements in SAR, the contemporary state of the art directs attention toward perspectives primarily centered on the general identification of people [8; 9; 10]. One of the principal goals inherent in Search and Rescue missions is to optimize the preservation of lives within the briefest conceivable timeframe. The initial hours after a disaster are characterized by heightened criticality, with the likelihood of locating survivors being at its peak during this period. Nonetheless, owing to the abrupt occurrence of these events, achieving the full preparedness of rescue teams for prompt deployment within a few hours and at a designated location poses a formidable challenge. Rescue teams have saved victims even ten days after a catastrophe, as exemplified by the earthquake in Turkey in February 2023. Notably, three individuals were rescued alive248 h after the event, as reported by CNN [11]. Furthermore, the challenging conditions in post-disaster environments impede the visual identification of victims during the initial assessment by a first-responder. These adverse conditions encompass scenarios such as victims being entirely or partially concealed by debris, the presence of immobile or unconscious victims, low or no illumination in the surroundings [12], potential gas leaks, and electrical hazards [13].  
In the state of the art, metrics are proposed for validating SAR missions, such as the one described by [PERSON] et al. [14], named **AAAVRoboCupRescue**, which involves parameters such as the number of located victims, the number of robots and operators involved, and the accuracy, following Equation (1). The current metrics for assessing the quality of a detection method primarily rely on the number of individuals found [15]. In the works by [PERSON] et al. [16; 17] and [PERSON] et al. [17], teh authors present approaches related to person detection but do not establish a comparison or specific metrics for validating the quality and implication of sensors in detections. While current methods score the missions considering victim detections, the current state of the art lacks a methodology to evaluate and compare the factors influencing this detection, especially by analyzing the three types of visual sensors (RGB, Thermal, and Multispectral).  
\[RobotRescueScore=(VictimsPoints\bigg{(}\frac{NumberOfRobots}{(1+NumberOfOperators)^{3}} \bigg{)}AverageAccuracy \tag{1}\]  
Combining different sensory sources has shown high effectiveness in extracting relevant information from analyzed elements, as seen in the work by [PERSON] et al., where they combine radar and optical satellite imagery to map lava flows [18]. It is noteworthy in the state-of-the-art literature that using RGB-thermal cameras is prevalent. Yet, the incorporation of multispectral bands simultaneously for this purpose has not been explored. This work takes as a basis the foundation for victim detection based on the works conducted by the authors in the domains of RGB [19], Thermal [20; 21], and Multispectral [22].