Search and rescue teams are constantly striving to improve their operations by developing new ways to quickly find a lost person. Search and rescue missions are divided into several types, such as mountain rescue, ground search and rescue, urban search and rescue, air and sea rescue, and combat search. The process is just as dangerous for the rescuer as it is for the victims. In the search for a missing person, every second matters; however, utilizing drones and the newest technologies, it is now possible to search for vast areas in a short time span, as well as places that are hard to reach, therefore increasing the rate of survivability while decreasing the search time. There are numerous object detection algorithms; however, only a small number of such algorithms can be used for search and rescue missions. The currently existing methods are unreliable; foreign objects are often recognized as objects of interest over the actual object of interest. The accuracy of such algorithms is usually determined by the quality of training. One study examined the effectiveness of drones in search and rescue operations in the mountains searching for lost people [3]. Two teams were formed for the study: the first team consisted of five professional certified rescuers, and the second team consisted of three rescuers with snowmobiles and one certified drone pilot. The first team performed the search and rescue mission on foot, following the guidelines of the traditional method. The second team used a drone for the search, and when a lost person was found, a team of three rescuers with snowmobiles was dispatched to retrieve the lost person. The efficiency indicators of the method in the article were detection time and the searched area. The first team, over 10 tests, detected a person on average in 3779 s and searched an area of 90,996 square meters, while the second team's average detection time was 531 s, and the average searched area was as many as 233,876 square meters. The results show the obvious advantage of using a drone in life-threatening situations. In a shorter period, a 2.57 times larger area of the territory is searched, and 54 min are saved. In another paper [4], the authors propose using a drone as an auxiliary tool in search and rescue missions to assess earthquake damage to infrastructure using cameras and thermal imaging cameras integrated into drones. According to the authors, obtaining information about infrastructure damage after an earthquake is very important for the rescue process and loss assessment. It often happens that when rescuing people from under the rubble, the remaining infrastructure collapses, and then the rescuers themselves have to be rescued. Using infrared thermal imaging technology to automatically detect structural damage to buildings and cracks in external walls, it is possible to quickly assess the risk, thereby reducing the number of further victims. In the article, the overall accuracy of the method ranges from 81 to 88 percent. A confusion matrix obtained by calculating real pixels on the ground surface and the corresponding pixels in the classification results was used for the evaluation. Another important reason for automating the search and rescue process is human factors. Statistically, 60-90 percent of all accidents are caused by human error [5; 6; 7]. In order to reduce the probability of human error and speed up the reaction time, scientists from various fields are studying image recognition algorithms, their applications, and automation possibilities. Today, image recognition is used in medicine [8; 9], self-driving cars [10], electrical wiring defect detection [11], construction industry [12], agriculture [13], and road engineering [14]. Thanks to these and many other scientific works, a trend has emerged of using high-performance image processing-based methods, such as machine learning and deep learning methods, which allow for processing huge amounts of data more simply and efficiently. Machine learning has been discussed by many researchers.