A Lightweight Radar–Camera Fusion Framework For Real-Time Drone Detection And Trajectory Prediction
| dc.contributor.author | Nishanth Aluvala 1NH22CS147 | |
| dc.contributor.author | P Krishna Kowshik Reddy 1NH22CS153 | |
| dc.contributor.author | Levaka Umar Reddy 1NH22CS117 | |
| dc.contributor.author | K Ashraf Ahmed 1NH22CS102 | |
| dc.date.accessioned | 2026-02-04T11:25:14Z | |
| dc.date.available | 2026-02-04T11:25:14Z | |
| dc.date.issued | 2026-02-04 | |
| dc.description.abstract | Unmanned aerial vehicles (UAVs), commonly known as drones, are increasingly used in civil, commercial, and security applications, leading to growing concerns related to airspace safety, privacy, and unauthorized intrusions. Traditional drone monitoring systems that rely on a single sensing modality often struggle under real-world conditions such as poor lighting, occlusions, cluttered backgrounds, or adverse weather, resulting in unreliable detection and tracking. To address these challenges, this project presents a lightweight radar–camera fusion framework for real-time drone detection and trajectory prediction. The system combines visual information from a monocular camera with motion-aware cues from radar data to achieve robust and consistent performance. A YOLOv8n model is employed for efficient drone detection from video frames, while a radar CNN processes range-Doppler maps to generate confidence scores that remain reliable even when visual cues degrade. | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/20888 | |
| dc.title | A Lightweight Radar–Camera Fusion Framework For Real-Time Drone Detection And Trajectory Prediction |