YOLOv9-AAG: Distinguishing Birds and Drones in Infrared and Visible Light Scenarios

This project focuses on creating a real-time system that can automatically identify whether an object in the sky is a bird or a drone. A normal laptop webcam is used to capture live video, which is then analyzed using a YOLO-based deep learning model. The model examines each frame, finds objects, draws a bounding box around them, and labels them accurately as either “Bird” or “Drone.” To make the system more useful in real situations, a small hardware setup is added using an ESP32 microcontroller, LEDs, and a buzzer. When a bird is detected, a green LED glows, and when a drone is detected, a red LED lights up and a buzzer produces an alert sound. This helps provide instant feedback without needing to look at the laptop screen.The full system is low-cost, easy to set up, and can be used in places like airports, farms, and security zones where early detection of drones or birds is important. By combining computer vision with simple electronics, this project shows how artificial intelligence can be applied in a practical, meaningful, and user-friendly way