Intelligent Monitoring and Route Adaptation System for Drones based on Neural Network Risk Analysis

The study examines an intelligent monitoring and route adaptation system for unmanned aerial vehicles (Drones) based on neural network risk analysis. The research considers autonomous navigation algorithms that enable environmental analysis and real-time trajectory correction in response to potential threats. The possibilities of applying computer vision, neural network algorithms, data preprocessing methods, object detection, semantic segmentation, trajectory planning algorithms, predictive control, and adaptive route optimization are assessed for identifying obstacles, moving objects, and restricted flight zones. The role of intelligent control systems for drones is analyzed, along with their impact on enhancing autonomy, resilience, and operational efficiency in dynamically changing environments. The proposed solutions are aimed at risk mitigation associated with emergency situations through the implementation of adaptive flight control strategies. The study employs methods of systems analysis, computer vision, and machine learning, including convolutional neural networks, image preprocessing algorithms, data filtering and segmentation, as well as sensor data analysis. The effectiveness assessment is carried out through trajectory modeling, testing of threat identification algorithms, and analysis of UAV route stability parameters. The scientific novelty lies in the development of an integrated system for intelligent UAV route correction based on neural network object classification methods and adaptive trajectory planning algorithms. Mechanisms for predictive risk analysis have been developed, ensuring automatic route adjustment upon detecting obstacles, adverse weather conditions, and restricted-access zones. The proposed control architecture integrates computer vision technologies, data stream analysis, and automated decision-making while using dynamic routing methods, real-time flight correction algorithms, and collision avoidance strategies. This approach enhances the level of UAV autonomy. The developed intelligent navigation algorithms can be implemented in modern autonomous UAV control systems, enabling adaptation to dynamic conditions and increasing the efficiency of task execution across various domains, including defense and industrial applications.

Decision-Making System, Intelligent Control, Neural Network Analysis, Computer Vision, Deep Learning, Autonomous Navigation, Threat Prevention, Route Optimization, Unmanned Aerial Vehicles, Adaptive Algorithms