DEEP LEARNING-BASED INTELLIGENT SYSTEM FOR DRONE AND UAV DETECTION IN SECURITY SURVEILLANCE APPLICATIONS

Abstract

The rapid growth in the use of unmanned aerial vehicles (UAVs) and drones has created new challenges for security and surveillance systems, particularly in sensitive areas such as military bases, airports, government facilities, and public events. Unauthorized or malicious drone activities can pose significant threats including privacy violations, smuggling, and potential attacks. To address these challenges, this paper presents a deep learning-based intelligent system for drone and UAV detection in security surveillance applications. The proposed system utilizes computer vision and deep learning techniques to automatically detect and identify drones from real-time video streams captured through surveillance cameras or embedded camera modules. A convolutional neural network (CNN)-based model is employed to analyze visual patterns and distinguish drones from other flying objects such as birds or aircraft. The system processes the captured images, performs object detection, and classifies UAV presence with high accuracy. When a drone is detected, the system can trigger alerts, enabling security personnel to take immediate action. The implementation integrates an embedded processing unit with a camera module to capture video data and transmit it to a trained deep learning model for inference. The model is trained using a dataset of drone images under different environmental conditions to improve detection robustness. Experimental results demonstrate that the proposed system provides efficient and reliable drone detection with improved accuracy and real-time performance, making it suitable for deployment in security surveillance environments. Overall, the proposed approach enhances situational awareness and strengthens perimeter security by providing an automated, intelligent, and scalable solution for monitoring and detecting unauthorized UAV activities.