For example, CutLER [30] leverages class-agnostic region proposals combined with contrastive learning to discover object masks without supervision. LOST [31] identifies salient regions via self-supervised attention maps, while DINO-based models [32] use vision transformerspretrained with self-distillation to generate class-agnostic object proposals. While these approaches achieve strong results on general benchmarks, they often rely on transformer backbones and computationally expensive pretraining, which limits their accessibility in real-time and low-resource scenarios, such as aerial search and rescue. Researchers in various fields are modifying and refining existing algorithms, continuously improving their accuracy. Recent developments in signal reconstruction and spatial feature learning across other domains offer relevant methodological insights. For example, [PERSON] et al. [33] introduced a dual-frequency LiDAR-based approach for 3D imaging, leveraging an all-phase Fourier transform to enhance signal recovery in compressed sensing scenarios. In another domain, [PERSON] et al. [34] presented a geometry-aware point cloud learning strategy for precise cutting-point detection in agricultural robotics. Although these studies address different application areas, they highlight the growing relevance of structured spatial representations and deep learning models for complex visual understanding tasks. In China, scientists adapted and improved the Yolov7 algorithm for human search and rescue purposes in open-water conditions, improving detection accuracy from 87.1 to 91.6% [35]. A different study was conducted to improve the accuracy of human detection in search and rescue operations in winter conditions by changing the input image resolution. Using higher-resolution images results in more accurate detection results, but in return, the detection time indicator deteriorates. According to the author, at a resolution of 224 \(\times\) 224, the detection accuracy ranges from 34 to 94 percent accuracy, and at a resolution of 3840 \(\times\) 2160, the accuracy is 70-94 percent [36]. In another paper [37], a new algorithm is developed based on the mean shift algorithm, applying it to search and rescue operations in the mountains. The original mean shift algorithm showed a result of 74.7% recall and 18.7% precision, while the newly developed CNN showed a result of 88.9% recall and 34.8% precision. Despite the fact that the result has improved significantly compared to the original algorithm in general, the final accuracy is relatively poor. Another drawback of this algorithm is that the processing of 40-50 photos, including human detection, took 1110 s, which emphasizes the relevance of our study. A common flaw of these works is that the training material is very similar to the material provided to the algorithm during testing. Such tests achieve a good accuracy rate but do not show the real accuracy of the algorithm in realistic circumstances. In the first work, the algorithm shows good accuracy in calm waves; however, most accidents occur in adverse weather conditions. The second work under consideration showed unstable results; the accuracy deteriorated when snow was not present or when foreign objects such as trees appeared in the frame. The third work under consideration shows the most realistic result, but due to its detection time, it is unsuitable for search and rescue missions, where every second counts. In this scientific work, the aim is to create an algorithm capable of detecting a person in the wild, based on footage from a drone or unmanned aerial vehicle. Conventional CNNs are highly accurate in performing stationary object recognition tasks but face difficulties in detecting moving objects. To solve this problem, we propose integrating a motion detection method into a CNN. Today, motion detection methods include improvements to the original methods, such as the frame difference method [38], the background subtraction method [39; 40] and the Horn-Schunck [41] and Lucas-Kanade [42] optical flow methods.