CLASSIFICATION AERIAL IMAGE USING IOT AND  DEEP LEARNING

Author(s)	Mr. REDDABOINA PRAVEEN, Dr. K KISHOR KUMAR
Country	India
Abstract	The use of unmanned aerial vehicles (UAVs) has brought drastic security issues in areas considered sensitive like defense zones, industrial installations, and restricted locations. This paper is a double-layered intelligent surveillance system with AI-based drone detection coupled with IoT-enabled ground object monitoring. The air detection module uses a Convolutional Neural Network (CNN) to analyze live video streams and detect unauthorized drones, and the ground detection module uses a Node MCU-driven ultrasonic sensor with a servo motor for 180° scanning. Both modules use Firebase cloud services to synchronize data in real-time and send instant mobile notifications to authorized personnel. The system provides end-to-end aerial and ground-level monitoring, providing scalability, cost-effectiveness, and quick response, and can thus be deployed to high- security contexts including borders, airports, and military bases.
Keywords	Drone images, Node MCU, CNN, Ultrasonic sensor, Drone Detection / IoT Surveillance YOLOv3, Real-Time Monitoring, Intrusion Detection, Surveillance System, Video Frame Analysis, Sensor Fusion, Node MCU, Firebase, Inertial Sensors, Object Recognition, Obstacle Avoidance, Cloud Alerting.
Field	Computer > Network / Security
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-10-11

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