Mass casualties under CBRN conditions are challenging scenarios. Warfare in Syria has shown that especially in urban situations access to casualties is difficult and will be challenging as time to operate in CBRN protective clothing is limited. This underlines the need for rapid detection of casualties as well as early monitoring of vital signs. A drone equipped with a radar in addition to an RGB and thermal cameras can be used to scan an area to quickly to detect injured humans and measure their respiration and heart rates, reporting them wirelessly to a base station. The project “UAV-Triage” follows this approach and addresses human action efficiency in two ways: 1. Search and rescue guidance became more efficient by directing responders to casualties while measuring the vital signs from a safe hygienic distance. 2. Thus, accelerating the search, rescue and decontamination before emergency treatment of the casualty. The developed drone system operates in two modes: scanning and monitoring. In scanning mode, the operator flies the drone, while the RGB and thermal cameras operate with human detection machine learning algorithms to detect humans. Then, the drone switches to monitoring mode, where it hovers 6 meters above individuals to estimate their vital signs by measuring mm and sub-mm movements of the human body, which corresponds to respiration and heart rates respectively. The radar processing relies on advanced signal processing filtering techniques to compensate for the drone vibration and extracting the vital signs signal. The system was tested on two healthy subjects lying on the ground and compared to a medical reference system to assess the system performance. Tests were conducted on each subject individually and also when the test subjects were positioned close to each other to assess the system’s capability to monitor the vital signs of multiple humans simultaneously. The results demonstrated the system's ability to detect vital signs, distinguishing between living and deceased casualties. The respiration rate was measured accurately with a 2 BPM error. The heart rate measurements were generally unreliable, but under calm wind conditions, the heart signal was detectable, indicating that using a more stable drone will improve heart rate estimation performance.