Object detection algorithm in a navigation system for a rescue drone

This article focuses on improving object recognition algorithms for rescue drones, in particular, enhancing the methodology for classifying human poses by expanding the set of key body points and using more effective mathematical models. A methodology is proposed that analyzes 11 key body points, enabling the classification of human positions (standing, lying down, sitting, kneeling, bent) with greater accuracy. Additionally, a gesture recognition algorithm is proposed, detecting gestures such as arm-waving as a signal for help, which increases the effectiveness of rescue operations. The paper also considers the possibilities of implementing the system on the limited hardware resources of onboard UAV computers. Using geometric relationships between body points reduces computational costs without sacrificing accuracy, making the proposed model suitable for real-world use. The conducted research confirms that the improved system can automatically assess victims’ conditions, prioritize rescue efforts, and optimize drone navigation. In future work, it is planned to integrate the algorithms with drones’ multisensory systems and test them in real-world conditions.