The integration of neural network methods in computer vision with logical infer- ence based on a Mivar expert system allows leveraging the advantages of both paradigms: high efficiency in processing unstructured visual data and the inter- pretability of decisions made based on formalized rules. An analysis of various computer vision tasks was conducted, demonstrating that OVD (Open-Vocabulary Detection) is the preferred tool for dynamic rescue operation scenarios. OVD pro- vides the best balance between the flexibility of detecting arbitrary categories, reliability when working with multiple objects, and the availability of data for training. Modern vision-language architectures, their features, and advantages were investigated. YOLO-World was selected as the base model, as it best meets the stringent requirements of real-time operation, achieving high processing speed while maintaining the flexibility of an open vocabulary. A fine-tuning procedure for the model was carried out, which included freezing the text encoder and the early layers of the convolutional backbone, as well as combining the Flickr30k, VisDrone, and SARD2 datasets. Using Flickr30k helped preserve the quality of the vision-language space, while the specialized datasets adapted the model to real-world application conditions. The fine-tuned model showed a significant in- crease in accuracy (mAP@50 rose from 0.0974 to 0.342, and mAP@50:95 from 0.0673 to 0.202), and also gained the ability to correctly recognize human poses specific to rescue operations and types of vehicles in drone imagery. A system of parameters and rules for the Mivar Expert System (MES) is proposed, which allows transitioning from simply listing objects in an image to a comprehensive situation assessment. This transforms the system into an active operator assistant, capable not only of detecting but also of interpreting threats. Thus, the developed hybrid intelligent system, combining the YOLO-World detector and the Mivar expert system, fully meets the stated goal and specified requirements: • Real-time operation due to the optimized architecture; • Flexibility of control through text prompts in natural language; • Interpretability and logical validity of decisions thanks to Mivar logical in- ference.