Багаторівнева архітектура системи автоматичного керування БПЛА для здійснення пошукових місій за відеоаналізом та металодетекцією

Abstract

The article presents a multi-level automatic mission control system for an unmanned aerial vehicle designed to detect hazardous items in tasks involving the identification of suspicious objects. The proposed architecture combines edge–ground–cloud data processing the onboard video camera and metal detector, as well as the use of vision–language models (ChatGPT-4.1 Vision, Gemini 2.5 Flash) for semantic verification of suspected objects. At the ground station, initial detection of hazardous items is performed using YOLOv8 and metal-detector signal analysis. Frames with intermediate confidence are then sent to the cloud for additional verification by VLMs. Based on the combined assessment, a decision is generated regarding the presence of a hazardous item, which automatically adjusts the UAV mission via MAVLink: the drone is switched AUTO to GUIDED mode, returns to the GPS coordinates of the suspicion, performs additional inspection, and then resumes the mission the saved waypoint. Experimental field tests with mock-ups of hazardous items demonstrated that combining YOLOv8, the metal detector, and VLMs makes it possible to achieve increasing precision to approximately 95.7% and maintaining near-real-time performance (effective 5 fps). The scientific novelty of the work lies in implementing a closed loop of “detection – semantic verification – automatic mission correction” for UAVs, which integrates multimodal data fusion with cloud-based AI models and reduces operator workload.