Adaptive Drone Identification and Neutralization Scheme for Real-Time Military Tactical Operations

Abstract

The surging proliferation in the deployment of unmanned aerial vehicles (UAVs) in various domains has resulted into unsolicited intrusion into private properties and protected areas thereby posing threat to national security. This paper proposed an adaptive scenario-based approach for detecting drone invasion using enhanced YOLOvS deep learning model to detect different drones and identify attached objects operating under any environment, size, speed, or shape. The dataset consists of 6 drone models and 8 attached weapons manually generated and preprocessed to form samples. In terms of accuracy, sensitivity, and timeliness, the result shows that our model achieved superior detection precision of 100%, sensitivity of 99.9%, F1-score of 87.2% for weapons identification at a shorter time of 0.021s than other models. The high detection accuracy undoubtedly makes our model well suited for real-time drone monitoring and countering of illegal drones in military offensives with minimal resource usage.