AI Powered Drone Surveillance System for Real Time Object Detection in Unmanned Aerial Vehicles Using Deep Learning Approaches

Deep learning algorithms, particularly CNNs, have significantly improved object detection accuracy in remote sensing applications. Unlike most UAV-based approaches, this project implements a static camera system utilizing CNN and YOLOv8 (a state-of-the-art one-stage detector) for real-time aerial image processing. The system is optimized for surveillance, environmental monitoring, and disaster response applications. While our current implementation uses fixed cameras, the architecture supports seamless UAV integration. This research examines the speed accuracy trade-offs between one-stage and two-stage detectors, demonstrating YOLOv8's ability to maintain both rapid inference and reliable detection performance. Experimental results validate the system's effectiveness in static deployments while its adaptability for dynamic drone-based scenarios.