Abstract
In a country, air defense systems are designed to reduce threats efficiently. An air defense system is a fundamental part of any country because it provides national security. This study presents an autonomous air defense system (AADS) development that will automatically detect aerial threats (e.g., drones) and target them without any human intervention. The AADS is implemented using radar, camera, and laser gun. The radar system dynamically emits microwaves and detects moving objects around it. It triggers the camera system if it senses the frequency of any aerial threat. The camera receives the radar's signal and detects using a neural network algorithm whether it is a threat or not. Neural network algorithms are used for the detection and classification of objects. The laser gun locks its target if the live video feed classifies an object as a more than 75% threat. In the detection stage, an average loss of 0.184961 was achieved using YOLOv3 and 0.155 using the Faster-RCNN. This system will ensure that no human errors are made while detecting threats in a region and improve national safety.