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Recent advancements in Unmanned Aerial Vehicles (UAVs) have transformed them into viable platforms for carrying sensors and computer systems capable of executing complex tasks. This research presents a method for implementing imaging and object recognition on standard hardware within an autonomous UAV to detect humans at a high frame rate in outdoor settings. By geo-locating these detected human positions, a map highlighting various points of interest can be generated. This saliency map can be invaluable in scenarios such as disaster relief operations, where it can assist in scheduling the delivery of medical supplies. The methodology has been successfully deployed and tested on the UAV Tech autonomous unmanned helicopter platform as part of a complete autonomous mission. This platform demonstrates stable flight characteristics and can effectively monitor and collect data in diverse regions. UAVs are increasingly utilized in hazardous environments, offering access to areas that are otherwise unreachable, facilitating tasks with remarkable ease. One of the primary applications of drones is in detecting human presence in areas affected by natural disasters, signaling nearby rescue teams via GSM technology. The outcomes of this study have enhanced the performance of this technique and have addressed its limitations
Krishna et al. (Thu,) studied this question.