Multimodal Autonomous Drone Surveillance for Campus Security: YOLO11-Based Person and ID-Card Detection with Acoustic Threat Classification

The main research contribution is the development of a single-drone multimodal campus surveillance system that combines autonomous waypoint flight, YOLO11-based person and visible ID-card detection, and acoustic threat classification in one working pipeline.

Its important contribution is mainly system integration rather than a new AI model. The visual and audio modules work together at the decision level so that either a missing visible ID card or a suspicious sound can trigger an alert for human review.

In simple terms, the study shows that drone navigation, visual detection, sound analysis, and security alerting can be combined into one practical campus-surveillance prototype.