Unmanned Aerial Vehicle (UAV) technology has revolutionized numerous sectors from aerial photography to geographical surveying. However, unauthorized drone operations pose significant security threats to sensitive areas and critical infrastructure. This paper presents an integrated real-time warning system that combines cutting-edge computer vision with multimodal artificial intelligence to address these challenges.
System Architecture
The system employs high-sensitivity electro-optical detection equipment including dual-spectrum cameras (infrared: 1280×1024 resolution, visible: 1920×1080 resolution) connected to a central processing unit (Intel i7-12700). The hardware configuration enables continuous monitoring of airspace within 1km radius under various environmental conditions.
The operational workflow implements a closed-loop process:
1. Electro-optical sensors capture real-time video streams
2. Detection algorithms identify drone targets
3. Tracking systems monitor movement trajectories
4. Multimodal analysis generates semantic alerts
5. Control signals adjust monitoring parameters
Algorithmic Framework
Vision-Based Detection and Tracking
The system employs YOLOv10 for real-time drone identification, achieving exceptional precision through architectural innovations. Target tracking integrates the SORT algorithm with Kalman filtering for trajectory prediction:
Kalman filter equations:
$$\begin{aligned}
&\hat{x}_t^- = F\hat{x}_{t-1} + Bu_{t-1} \\
&P_t^- = FP_{t-1}F^T + Q \\
&K_t = P_t^-H^T(HP_t^-H^T + R)^{-1} \\
&\hat{x}_t = \hat{x}_t^- + K_t(z_t – H\hat{x}_t^-) \\
&P_t = (I – K_tH)P_t^-
\end{aligned}$$
Where $F$ denotes state transition matrix, $Q$ process noise covariance, $R$ measurement noise covariance, and $K_t$ represents the Kalman gain. This framework enables robust tracking of drones moving at velocities up to 23m/s.
Multimodal Semantic Analysis
For bandwidth-constrained scenarios, the system incorporates GLM-4V to transform visual data into compressed semantic descriptions. Structured prompt templates convert detection parameters into contextual alerts:
| Component | Prompt Structure | Output Example |
|---|---|---|
| Background | “Current view shows {…} sky with {…} below. Distant elements include {…}” | “Clear blue sky above dense forest canopy with distant structural features” |
| Drone Identification | “Identified UAV exhibits {…} physical characteristics” | “Quadcopter configuration with black chassis and red indicator lights” |
| Positional Data | “UAV located at coordinates (x,y) relative to view center” | “Detected at coordinates (-547.0, 131.5) pixels” |
Performance Evaluation
Detection and Tracking Accuracy
Comparative analysis demonstrates YOLOv10’s superiority in drone technology applications:
| Algorithm | mAP (%) | FPS |
|---|---|---|
| YOLOv10 | 97.6 | 68.96 |
| YOLOv8 | 88.3 | 59.5 |
| YOLOv7 | 82.1 | 50.8 |
| YOLOv4 | 83.9 | 28.0 |
The system maintains 30FPS real-time processing while tracking multiple Unmanned Aerial Vehicles at maximum operational range (1km). Prompt engineering significantly enhanced semantic accuracy with template-guided descriptions achieving 92% contextual relevance versus 67% in unstructured outputs.
Operational Efficiency
Bandwidth optimization represents a critical advancement in drone surveillance technology. Semantic encoding reduces data payload by 94% compared to video transmission:
Payload reduction ratio:
$$\Delta P = 1 – \frac{S_s}{S_v}$$
Where $S_s$ denotes semantic payload size (avg. 2.3KB) and $S_v$ represents equivalent video frame data (avg. 38.7KB). This efficiency enables reliable operation in bandwidth-limited environments below 5Mbps.
Implementation and Results
The integrated software platform developed with PYQT5 framework provides comprehensive operational control. Field tests conducted with DJI Phantom 4 Pro and Mavic Air 2 drones validated system performance across diurnal cycles and variable weather conditions. The solution demonstrates particular effectiveness in complex urban environments where traditional radar-based systems encounter limitations.
Conclusion
This integrated system establishes a new standard for anti-drone technology through its synergistic combination of computer vision and multimodal AI. The implementation of YOLOv10 provides unprecedented detection accuracy (97.6% mAP) while GLM-4V transformation enables bandwidth-efficient semantic alerting. This dual-mode approach effectively addresses the critical challenge of maintaining surveillance integrity under variable network conditions. The solution demonstrates significant potential for securing sensitive airspace against unauthorized Unmanned Aerial Vehicle operations while establishing a scalable framework for future enhancements in drone detection technology.