Abstract:
This project presents the development of a Smart Surveillance Drone System utilizing mmWave sensor technology for the real-time detection of humans, animals, and birds in diverse environmental conditions. Traditional camera-based surveillance systems often fail in scenarios involving low light, fog, or visual obstructions. To address these limitations, a mm-Wave sensor is employed, operating independently of lighting conditions and providing reliable data through radio wave reflections. The detection mechanism is based on a threshold approach rather than complex AI models, ensuring the system remains lightweight and power-efficient. The mm-Wave sensor is integrated with a Raspberry Pi 4, selected for its processing capability and portability. This combination is mounted on a drone chosen for its payload capacity to carry the sensor and power supply. To ensure extended operational time without reliance on heavy batteries, a power bank is used, allowing flexibility in remote environments. The system provides accurate detection in challenging environments while avoiding unnecessary complexity, making it scalable and cost-effective. The solution offers valuable applications in disaster zones, wildlife monitoring, border surveillance, and patrolling. By focusing on practicality and robustness, the surveillance system bridges a critical gap in modern monitoring technologies where traditional vision-based systems fall short.
